Suchergebnis: Katalogdaten im Herbstsemester 2017

Agrarwissenschaften Bachelor Information
Bachelor-Studium (Studienreglement 2016)
1. Semester
Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0251-00LMathematik I: Analysis I und Lineare AlgebraO6 KP4V + 2UL. Halbeisen
KurzbeschreibungDiese Vorlesung behandelt mathematische Konzepte und Methoden, die zum Modellieren, Lösen und Diskutieren wissenschaftlicher Probleme nötig sind - speziell durch gewöhnliche Differentialgleichungen.
LernzielMathematik ist von immer grösserer Bedeutung in den Natur- und Ingenieurwissenschaften. Grund dafür ist das folgende Konzept zur Lösung konkreter Probleme: Der entsprechende Ausschnitt der Wirklichkeit wird in der Sprache der Mathematik modelliert; im mathematischen Modell wird das Problem - oft unter Anwendung von äusserst effizienter Software - gelöst und das Resultat in die Realität zurück übersetzt.

Ziel der Vorlesungen Mathematik I und II ist es, die einschlägigen mathematischen Grundlagen bereit zu stellen. Differentialgleichungen sind das weitaus wichtigste Hilfsmittel im Prozess des Modellierens und stehen deshalb im Zentrum beider Vorlesungen.
Inhalt1. Differential- und Integralrechnung:
Wiederholung der Ableitung, Linearisierung, Taylor-Polynome, Extremwerte, Stammfunktion, Hauptsatz der Differential- und Integralrechnung, Integrationsmethoden, uneigentliche Integrale.

2. Lineare Algebra und Komplexe Zahlen:
lineare Gleichungssysteme, Gauss-Verfahren, Matrizen, Determinanten, Eigenwerte und Eigenvektoren, Darstellungsformen der komplexe Zahlen, Potenzieren, Radizieren, Fundamentalsatz der Algebra.

3. Gewöhnliche Differentialgleichungen:
Separierbare Differentialgleichungen (DGL), Integration durch Substitution, Lineare DGL erster und zweiter Ordnung, homogene Systeme linearer DGL mit konstanten Koeffizienten, Einführung in die dynamischen Systeme in der Ebene.
Literatur- Thomas, G. B., Weir, M. D. und Hass, J.: Analysis 1, Lehr- und Übungsbuch (Pearson).
- Gramlich, G.: Lineare Algebra, eine Einführung (Hanser).
- Papula, L.: Mathematik für Ingenieure und Naturwissenschaftler, Bd. 1 und 2 (Vieweg+Teubner).
Voraussetzungen / BesonderesVoraussetzungen: Vertrautheit mit den Grundlagen der Analysis, insbesondere mit dem Funktions- und Ableitungsbegriff.

Mathe-Lab (Präsenzstunden):
Mo 12-14, Di 17-19, Mi 17-19, stets im Raum HG E 41.
Grundlagenfächer (zweiter Studienjahr)
Prüfungsblock
NummerTitelTypECTSUmfangDozierende
701-0071-00LMathematik III: SystemanalyseO4 KP2V + 1UN. Gruber, M. Vogt
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
Bachelor-Studium (Studienreglement 2010)
5. Semester
Methodenfächer
NummerTitelTypECTSUmfangDozierende
751-0441-00LWissenschaftliche Datenauswertung und -präsentationO2 KP2GW. Eugster
KurzbeschreibungDiese Veranstaltung macht die Studierenden mit den Schritten von der Dateneingabe über statistischen Analyseverfahren bis zu grafischen Darstellungsformen vertraut. In Übungen mit der Daten-Analyse-Software R (via RStudio) wird das methodische Werkzeug zur Daten-Auswertung und -Präsentation erklärt. Daten aus einem Versuch mit Prof. E. Frossard aus dem Vorsemester werden verwendet und diskutiert.
LernzielDiese Veranstaltung soll die Studierenden mit den statistischen Analyseverfahren, die im Rahmen einer Bachelorarbeit benötigt werden (deskriptive Statistik, linear Regression usw.) vertraut machen und ihnen Gelegenheit bieten, im Rahmen geleiteter praktischer Übungen mit der Daten-Analyse-Software R anhand ausgewählter Beispiele das methodische Werkzeug zur Daten-Auswertung und -Präsentation kennen zu lernen. Ein wichtiger Schwerpunkt wird die Vermittlung geeigneter grafischer Darstellungsarten sein (wie präsentiert man Daten anschaulich und wissenschaftlich korrekt?).
InhaltVoraussichtliche Kursschwerpunkte:
1. Einführung
2. Datenerfassung, -organisation, -pflege, Arbeit mit Daten
3. Grafische Darstellungen I - Tabellenkalkulation
4. Vorbereitung Daten aus Kurs mit Prof. E. Frossard / 4. Sem.
5. Korrekte und problematische grafische Darstellungen
6. Einführung in 'R'
7. Daten einlesen und darstellen
8. Verteilungen und Konfidenzintervalle
9. Statistische Tests - Repetition und Anwendung
10. Lineare Regression
11./12. Analysis of Variance
13. ANOVA - Diskussion der Resultate mit Prof. E. Frossard

In der letzten Doppelstunde: Leistungskontrolle
SkriptHauptsächlich Deutsch (mit englischen Abschnitten aus Lehrbüchern)
Voraussetzungen / BesonderesTheoretisches Wissen in Statistik aus der Vorlesung mit Übungen des 4. Semesters; erfüllte Leistungskontrolle dieser Veranstaltung
Agrarwissenschaften Master Information
Master-Studium (Studienreglement 2016)
Vertiefung Agrarökonomie
Methodische Kompetenzbereiche
Methods in Agricultural Economics
NummerTitelTypECTSUmfangDozierende
751-0423-00LRisk Analysis and Risk Management in AgricultureW+3 KP2GR. Finger
KurzbeschreibungAgricultural production is exposed to various risks which are important for decisions taken by farmers and other actors in the agri-food sector. Moreover, risk management is indispensable for all actors. This course introduces modern concepts on decision making under risk and recent developments in risk management. The focus of this course in on agriculture applications.
Lernziel-to develop a better understanding of decision making under uncertainty and risk;
-to gain experience in different approaches to analyze risky decisions;
-to develop an understanding for different sources of risk in agricultural production;
-to understand the crucial role of subjective perceptions and preferences for risk management decisions;
-to get an overview on risk management in the agricultural sector, with a particular focus on insurance solutions
Inhalt- Quantification and measurement of risk
- Risk preferences, expected utility theory and alternative models of risk behavior
- Concepts on the decision making under risk
- Production, investment and diversification decisions under risk
- Risk management in agriculture
SkriptHandouts will be distributed in the lecture and available on the moodle.
Voraussetzungen / Besonderesknowledge of basic concepts of probability theory and microeconomics
Ergänzungen
Agricultural Economics and Policy
NummerTitelTypECTSUmfangDozierende
751-0423-00LRisk Analysis and Risk Management in AgricultureW3 KP2GR. Finger
KurzbeschreibungAgricultural production is exposed to various risks which are important for decisions taken by farmers and other actors in the agri-food sector. Moreover, risk management is indispensable for all actors. This course introduces modern concepts on decision making under risk and recent developments in risk management. The focus of this course in on agriculture applications.
Lernziel-to develop a better understanding of decision making under uncertainty and risk;
-to gain experience in different approaches to analyze risky decisions;
-to develop an understanding for different sources of risk in agricultural production;
-to understand the crucial role of subjective perceptions and preferences for risk management decisions;
-to get an overview on risk management in the agricultural sector, with a particular focus on insurance solutions
Inhalt- Quantification and measurement of risk
- Risk preferences, expected utility theory and alternative models of risk behavior
- Concepts on the decision making under risk
- Production, investment and diversification decisions under risk
- Risk management in agriculture
SkriptHandouts will be distributed in the lecture and available on the moodle.
Voraussetzungen / Besonderesknowledge of basic concepts of probability theory and microeconomics
Master-Studium (Studienreglement 2011)
Vertiefungen
Vertiefung in Food and Resource Use Economics
Methodische Kompetenzbereiche
Methods in Food and Resource Use Economics
NummerTitelTypECTSUmfangDozierende
751-0423-00LRisk Analysis and Risk Management in AgricultureW+3 KP2GR. Finger
KurzbeschreibungAgricultural production is exposed to various risks which are important for decisions taken by farmers and other actors in the agri-food sector. Moreover, risk management is indispensable for all actors. This course introduces modern concepts on decision making under risk and recent developments in risk management. The focus of this course in on agriculture applications.
Lernziel-to develop a better understanding of decision making under uncertainty and risk;
-to gain experience in different approaches to analyze risky decisions;
-to develop an understanding for different sources of risk in agricultural production;
-to understand the crucial role of subjective perceptions and preferences for risk management decisions;
-to get an overview on risk management in the agricultural sector, with a particular focus on insurance solutions
Inhalt- Quantification and measurement of risk
- Risk preferences, expected utility theory and alternative models of risk behavior
- Concepts on the decision making under risk
- Production, investment and diversification decisions under risk
- Risk management in agriculture
SkriptHandouts will be distributed in the lecture and available on the moodle.
Voraussetzungen / Besonderesknowledge of basic concepts of probability theory and microeconomics
Atmospheric and Climate Science Master Information
Module
Hydrologie und Wasserkreislauf
NummerTitelTypECTSUmfangDozierende
701-1253-00LAnalysis of Climate and Weather Data Information W3 KP2GC. Frei
KurzbeschreibungObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
LernzielObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
InhaltIntroduction into the theoretical background and the practical application of methods of data analysis in meteorology and climatology.

Topics: exploratory methods, hypothesis testing, analysis of climate trends, measuring the skill of climate and forecasting models, analysis of extremes, principal component analysis and maximum covariance analysis.

The lecture also provides an introduction into R, a programming language and graphics tool frequently used for data analysis in meteorology and climatology. During hands-on computer exercises the student will become familiar with the practical application of the methods.
SkriptDocumentation and supporting material include:
- documented view graphs used during the lecture
- excercise sets and solutions
- R-packages with software and example datasets for exercise sessions

All material is made available via the lecture web-page.
LiteraturSuggested literature:
- Wilks D.S., 2005: Statistical Methods in the Atmospheric Science. (2nd edition). International Geophysical Series, Academic Press Inc. (London)
- Coles S., 2001: An introduction to statistical modeling of extreme values. Springer, London. 208 pp.
Voraussetzungen / BesonderesPrerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren.
Ergänzungen
Ergänzung in nachhaltiger Energienutzung
NummerTitelTypECTSUmfangDozierende
227-1631-00LEnergy System Analysis Information W4 KP3GG. Hug, S. Hellweg, F. Noembrini, A. Schlüter
KurzbeschreibungThe course provides an introduction to the methods and tools for analysis of energy consumption, energy production and energy flows. Environmental aspects are included as well as economical considerations. Different sectors of the society are discussed, such as electric power, buildings, and transportation. Models for energy system analysis planning are introduced.
LernzielThe purpose of the course is to give the participants an overview of the methods and tools used for energy systems analysis and how to use these in simple practical examples.
InhaltThe course gives an introduction to methods and tools for analysis of energy consumption, energy production and energy flows. Both larger systems, e.g. countries, and smaller systems, e.g. industries, homes, vehicles, are studied. The tools and methods are applied to various problems during the exercises. Different conventions of energy statistics used are introduced.

The course provides also an introduction to energy systems models for developing scenarios of future energy consumption and production. Bottom-up and Top-Down approaches are addressed and their features and applications discussed.

The course contains the following parts:
Part I: Energy flows and energy statistics
Part II: Environmental impacts
Part III: Electric power systems
Part IV: Energy in buildings
Part V: Energy in transportation
Part VI: Energy systems models
SkriptHandouts
LiteraturExcerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
701-0071-AALMathematics III: Systems Analysis
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RN. Gruber
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
701-1901-AALSystems Analysis
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RN. Gruber
KurzbeschreibungSystems analysis is about the application of mathematical concepts to solve real world problems in a quantitative manner. Areas covered include: Dynamic linear models with one and several variables, Non-linear models with one or several variables; discrete-time models; and continuous models in space and time.
LernzielThe goal of the course is to develop quantitative skills in order to understand and solve a range of typical environmental problems.
InhaltThe subject of the exam is the content of my
undergraduate lecture series Systemanalyse I and II (see http://www.up.ethz.ch/education/system_analysis/index_DE).
This course is closely aligned with the Imboden&Koch / Imboden&Pfenniger books, except that I essentially skip chapter 7.
SkriptNo script is available, but you can purchase the Imboden/Koch or Imboden/Pfenniger books (or download some of the chapters yourself) through the Springer Verlag:

English version:
http://link.springer.com/book/10.1007/978-3-642-30639-6/page/1

German version:
http://www.springer.com/environment/book/978-3-540-43935-6
Bauingenieurwissenschaften Bachelor Information
Bachelor-Studium (Studienreglement 2014)
Obligatorische Fächer des Basisjahres
Basisprüfung
Anstelle der deutschsprachigen Lehrveranstaltung 851-0703-03L Grundzüge des Rechts für Bauwissenschaften kann wahlweise auch die französischsprachige Lehrveranstaltung 851-0709-00L Droit civil belegt werden.
NummerTitelTypECTSUmfangDozierende
401-0241-00LAnalysis I Information O7 KP5V + 2UM. Akka Ginosar
KurzbeschreibungMathematische Hilfsmittel des Ingenieurs
LernzielMathematik als Hilfsmittel zur Lösung von Ingenieurproblemen:
Verständnis für mathematische Formulierung von technischen und naturwissenschaftlichen Problemen.
Erarbeitung des mathematischen Grundwissens für einen Ingenieur.
InhaltKomplexe Zahlen.
Differentialrechnung und Integralrechnung für Funktionen einer Variablen mit Anwendungen.
Einfache mathematische Modelle in den Naturwissenschaften.
SkriptDie Vorlesung folgt weitgehend

Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer; online verfügbar unter:
http://link.springer.com/book/10.1007/978-3-8348-2559-9/page/1
LiteraturNeben Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer sind auch die folgenden Bücher/Skripte empfehlenswert und decken den zu behandelnden Stoff ab:

Tilo Arens et al., "Mathematik", Springer; online verfügbar unter:
http://link.springer.com/book/10.1007/978-3-642-44919-2/page/1

Meike Akveld, "Analysis 1", vdf;
http://vdf.ch/index.php?route=product/product&product_id=1706

Urs Stammbach, "Analysis I/II" (erhältlich im ETH Store);
https://people.math.ethz.ch/~stammb/analysisskript.html
Obligatorische Fächer 3. Semester
Prüfungsblock 1
NummerTitelTypECTSUmfangDozierende
401-0243-00LAnalysis IIIO3 KP2V + 1UA. Sisto
KurzbeschreibungWe will model and solve scientific problems with partial differential equations. Differential equations which are important in applications will be classified and solved. Elliptic, parabolic and hyperbolic differential equations will be treated. The following mathematical tools will be introduced: Laplace and Fourier transforms, Fourier series, separation of variables, methods of characteristics.
LernzielLearning to model scientific problems using partial differential equations and developing a good command of the mathematical methods that can be applied to them. Knowing the formulation of important problems in science and engineering with a view toward civil engineering (when possible). Understanding the properties of the different types of partial differential equations arising in science and in engineering.
InhaltClassification of partial differential equations

Study of the Heat equation general diffusion/parabolic problems using the following tools:
* Separation of variables
* Fourier series
* Fourier transform
* Laplace transform

Study of the wave equation and general hyperbolic problems using similar tools and the method of characteristics.

Study of the Laplace equation and general elliptic problems using similar tools and generalizations of Fourier series.
LiteraturThe course material is taken from the following sources:

Stanley J. Farlow - Partial Differential Equations for Scientists and Engineers

G. Felder: Partielle Differenzialgleichungen.
https://people.math.ethz.ch/~felder/PDG/
Voraussetzungen / BesonderesAnalysis I and II. In particular, knowing how to solve ordinary differential equations is an important prerequisite.
Bauingenieurwissenschaften Master Information
1. Semester
Vertiefungsfächer
Vertiefung in Konstruktion
NummerTitelTypECTSUmfangDozierende
101-0187-00LStructural Reliability and Risk Analysis Information W3 KP2GS. Marelli
KurzbeschreibungStructural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.
LernzielThe goal of this course is to provide the students with a thorough understanding of the key concepts behind structural reliability and risk analysis. After this course the students will have refreshed their knowledge of probability theory and statistics to model uncertainties in view of engineering applications. They will be able to analyze the reliability of a structure and to use risk assessment methods for decision making under uncertain conditions. They will be aware of the state-of-the-art computational methods and software in this field.
InhaltEngineers are confronted every day to decision making under limited amount of information and uncertain conditions. When designing new structures and systems, the design codes such as SIA or Euro- codes usually provide a framework that guarantees safety and reliability. However the level of safety is not quantified explicitly, which does not allow the analyst to properly choose between design variants and evaluate a total cost in case of failure. In contrast, the framework of risk analysis allows one to incorporate the uncertainty in decision making.

The first part of the course is a reminder on probability theory that is used as a main tool for reliability and risk analysis. Classical concepts such as random variables and vectors, dependence and correlation are recalled. Basic statistical inference methods used for building a probabilistic model from the available data, e.g. the maximum likelihood method, are presented.

The second part is related to structural reliability analysis, i.e. methods that allow one to compute probabilities of failure of a given system with respect to prescribed criteria. The framework of reliability analysis is first set up. Reliability indices are introduced together with the first order-second moment method (FOSM) and the first order reliability method (FORM). Methods based on Monte Carlo simulation are then reviewed and illustrated through various examples. By-products of reliability analysis such as sensitivity measures and partial safety coefficients are derived and their links to structural design codes is shown. The reliability of structural systems is also introduced as well as the methods used to reassess existing structures based on new information.

The third part of the course addresses risk assessment methods. Techniques for the identification of hazard scenarios and their representation by fault trees and event trees are described. Risk is defined with respect to the concept of expected utility in the framework of decision making. Elements of Bayesian decision making, i.e. pre-, post and pre-post risk assessment methods are presented.

The course also includes a tutorial using the UQLab software dedicated to real world structural reliability analysis.
SkriptSlides of the lectures are available online every week. A printed version of the full set of slides is proposed to the students at the beginning of the semester.
LiteraturAng, A. and Tang, W.H, Probability Concepts in Engineering - Emphasis on Applications to Civil and Environmental Engineering, 2nd Edition, John Wiley & Sons, 2007.

S. Marelli, R. Schöbi, B. Sudret, UQLab user manual - Structural reliability (rare events estimation), Report UQLab-V0.92-107.
Voraussetzungen / BesonderesBasic course on probability theory and statistics
3. Semester
Vertiefungsfächer
Vertiefung in Bau- und Erhaltungsmanagement
NummerTitelTypECTSUmfangDozierende
101-0439-00LIntroduction to Economic Analysis - A Case Study Approach with Cost Benefit Analysis in Transport
Remark:
Former Title "Introduction to Economic Policy - A Case Study Approach with Cost Benefit Analysis in Transport".
W6 KP4GK. W. Axhausen, R. Schubert
KurzbeschreibungDie Vorlesung stellt einige grundlegende ökonomische Prinzipien sowie die Verfahren der Kosten-Nutzen-Analyse vor; sie führt auch in Methoden zur Ermittlung von Bewertungsgrössen ein
LernzielSichere Kenntnis mikro- und makroökonomischer Grundlagen. Erarbeitung und Übung von Verfahren der Bewertung von Massnahmen und infrastrukturellen Ausbauten
InhaltMikro-und makroökonomische Grundlagen; Kosten - Nutzen - Analyse; Nutzwertanalyse; Europäische Richtlinien; Stated response Verfahren; Reisekostenansatz et al.; Bewertung von Reisezeitveränderungen; Bewertung der Verkehrssicherheit
Skriptmoodle Plattform für die ökonomischen Grundlagen; Umdrucke
LiteraturTaylor, M.P., Mankiw, N.G. (2014): Economics; Harvard Press

VSS (2006) SN 640 820: Kosten-Nutzen-Analysen im Strassenverkehr, VSS, Zürich.

Boardman, A.E., D.H. Greenberg, A.R. Vining und D.L. Weimer (2001) Cost – Benefit – Analysis: Concepts and Practise, Prentice-Hall, Upper Saddle River.

ecoplan and metron (2005) Kosten-Nutzen-Analysen im Strassenverkehr: Kommentar zu SN 640 820, UVEK, Bern.
Vertiefung in Konstruktion
NummerTitelTypECTSUmfangDozierende
101-0179-00LProbabilistic Seismic Risk Analysis and Management for Civil Systems
Findet dieses Semester nicht statt.
W3 KP2GB. Stojadinovic, Noch nicht bekannt
KurzbeschreibungAdvanced topics covered in this course are: 1) probabilistic seismic hazard analysis; 2) probabilistic seismic risk analysis; 3) seismic risk management using structural and financial engineering means; and, time permitting, 4) advanced topics in systemic probabilistic risk evaluation.
LernzielAfter successfully completing this course the students will be able to:

1. Gather the necessary data and conduct a probabilistic seismic hazard analysis for a site.
2. Gather the necessary data and conduct a probabilistic vulnerability analysis of a building or an element of a civil infrastructure system at a site.
3. Design structural and/or financial engineering solutions to mitigate the seismic risk at a site.
InhaltThis course extends the series of two courses on seismic design of structures at ETHZ and introduces the topic of probabilistic seismic risk analysis and seismic risk management for the build environment and civil infrastructure systems. The following advanced topics will be covered in this course: 1) probabilistic seismic hazard analysis; 2) probabilistic seismic risk analysis; 3) seismic risk management using structural and financial engineering means; and, time permitting, 4) advanced topics in systemic probabilistic risk evaluation.
SkriptThe electronic copies of the learning material will be uploaded to ILIAS and available through myStudies. This will include the lecture notes, additional reading, and exercise problems and solutions. There is no textbook for this course.
LiteraturReading material:
- Jack R Benjamin, C. Allin Cornell (2014) Probability, Statistics, and Decision for Civil Engineers
- A. H-S. Ang (Author), W. H. Tang Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering
- P.E. Pinto, R. Giannini and P. Franchin (2004) Seismic reliability analysis of structures, IUSSPress. Pavia;
- McGuire, R.K. 2004. Seismic hazard and risk analysis: EERI Monograph MNO-10, Earthquake Engineering Research Institute.
- A Mc. Neil, R. Frey and P. Embrechts, Quantitative Risk Management, Concepts, Techniques and Tools, Princeton University Press, 2015
- R. Rees, A. Wambach, The Microeconomics of Insurance, Foundations and Trends in Microeconomics, Vol. 4, Mps. 1-2 (2008), pp. 1- 163, DOI: 10.1561/0700000023
- Earthquake Engineering: From Engineering Seismology to Performance-Based Engineering, Yousef Borzorgnia and Vitelmo Bertero, Eds., CRC Press, 2004
- Dynamics of Structures: Theory and Applications to Earthquake Engineering, 4th edition, Anil Chopra, Prentice Hall, 2012
- Erdbebensicherung von Bauwerken, 2nd edition, Hugo Bachmann, Birkhäuser, Basel, 2002

References:
-Norm SIA 261: Einwirkungen auf Tragwerke (Actions on Structures). Schweizerischer Ingenieur- und Architekten-Verein, Zürich, 2003

Software:
- Bispec: software for unidirectional and bidirectional dynamic time-history and spectral seismic analysis of a simple dynamic system. http://eqsols.com/Bispec.aspx
- SAP2000 v15.1: general-purpose 3D nonlinear structural analysis software. http://www.csiberkeley.com/sap2000
- OpenSees: Open System for Earthquake Engineering Simulation, is an object-oriented, open- source software framework. http://opensees.berkeley.edu/
Voraussetzungen / BesonderesETH Seismic Design of Structures I course (101-0188-00), or equivalent. Students are expected to understand the seismological nature of earthquakes, to characterize the ground motion excitation, to analyze the response of elastic single- and multiple-degree-of-freedom systems to earthquake excitation, to use the concept of response and design spectrum, to compute the equivalent seismic loads on simple structures, and to perform code-based seismic design of simple structures.
Vertiefung in Verkehrssysteme
NummerTitelTypECTSUmfangDozierende
101-0439-00LIntroduction to Economic Analysis - A Case Study Approach with Cost Benefit Analysis in Transport
Remark:
Former Title "Introduction to Economic Policy - A Case Study Approach with Cost Benefit Analysis in Transport".
W6 KP4GK. W. Axhausen, R. Schubert
KurzbeschreibungDie Vorlesung stellt einige grundlegende ökonomische Prinzipien sowie die Verfahren der Kosten-Nutzen-Analyse vor; sie führt auch in Methoden zur Ermittlung von Bewertungsgrössen ein
LernzielSichere Kenntnis mikro- und makroökonomischer Grundlagen. Erarbeitung und Übung von Verfahren der Bewertung von Massnahmen und infrastrukturellen Ausbauten
InhaltMikro-und makroökonomische Grundlagen; Kosten - Nutzen - Analyse; Nutzwertanalyse; Europäische Richtlinien; Stated response Verfahren; Reisekostenansatz et al.; Bewertung von Reisezeitveränderungen; Bewertung der Verkehrssicherheit
Skriptmoodle Plattform für die ökonomischen Grundlagen; Umdrucke
LiteraturTaylor, M.P., Mankiw, N.G. (2014): Economics; Harvard Press

VSS (2006) SN 640 820: Kosten-Nutzen-Analysen im Strassenverkehr, VSS, Zürich.

Boardman, A.E., D.H. Greenberg, A.R. Vining und D.L. Weimer (2001) Cost – Benefit – Analysis: Concepts and Practise, Prentice-Hall, Upper Saddle River.

ecoplan and metron (2005) Kosten-Nutzen-Analysen im Strassenverkehr: Kommentar zu SN 640 820, UVEK, Bern.
Biologie Bachelor Information
2. Studienjahr, 3. Semester
Kernfächer
NummerTitelTypECTSUmfangDozierende
551-1003-00LMethoden der Biologischen Analytik Information O3 KP3GR. Aebersold, M. Badertscher, K. Weis
Kurzbeschreibung529-1042-00
Grundlagen der wichtigsten Trennmethoden und der Interpretation von Molekülspektren.

551-1003-00
Der Kurs befasst sich mit den Methoden und ausgewählten Anwendungen von Methoden der Nukleinsäuresequenzierung, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Lernziel529-1042-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten für den Einsatz von relevanten spektroskopischen und Trennmethoden in der analytisch-chemischen Praxis.

551-1003-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten der Methoden für die Bestimmung von Nukleinsäuresequenzen, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Inhalt529-1042-00
Anwendungsorientierte Grundlagen der organischen Instrumentalanalytik und des empirischen Einsatzes von Methoden der Strukturaufklärung (Massenspektrometrie, NMR-, IR-, UV/VIS-Spektroskopie). Grundlagen und Anwendung chromatographischer und elektrophoretischer Trennverfahren. Praxisnahe Anwendung und Vertiefung des Grundwissens anhand von Übungen.

551-1003-00
Der Kurs setzt sich zusammen aus Vorlesungen, die die theoretischen und technischen Grundlagen der betreffenden analytischen Methoden vermitteln und Übungen, die sich mit den Anwendungen der analytischen Methoden in der modernen experimentellen Biologie befassen.
Skript529-1042-00
Ein umfangreiches Skript ist im HCI-Shop erhältlich. Eine Kurzfassung des Teils "Spektroskopie" definiert die für die Prüfung dieses Teils relevanten Themen.
Literatur529-1042-00
- Pretsch E., Bühlmann P., Badertscher M. Structure Determination of Organic Compounds, 5th revised and enlarged English edition, Springer-Verlag, Berlin 2009;
- Pretsch E., Bühlmann P., Badertscher M., Spektroskopische Daten zur Strukturaufklärung organischer Verbindungen, fünfte Auflage, Springer-Verlag, Berlin 2010;
- D.A. Skoog, J.J. Leary, Instrumentelle Analytik, Grundlagen, Geräte, Anwendungen, Springer, Berlin, 1996;
- K. Cammann, Instrumentelle Analytische Chemie, Verfahren, Anwendungen, Qualitätssicherung, Spektrum Akademischer Verlag, Heidelberg, 2001;
- R. Kellner, J.-M. Mermet, M. Otto, H.M. Widmer, Analytical Chemistry, Wiley-VCH Verlag, Weinheim, 1998;
- K. Robards, P.R.Haddad, P.E. Jackson, Principles and practice of modern chromatographic methods, Academic Press, London, 1994;
Voraussetzungen / Besonderes529-1042-00
Voraussetzungen:
- 529-1001-01 V "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1001-00 P "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1011-00 G "Organische Chemie I (für Biol./Pharm.Wiss.)"
3. Studienjahr, 5. Semester
Blockkurse
Anmeldung zu Blockkursen muss zwingend über die website https://www.uzh.ch/zoolmed/ssl-dir/Blockkurse_UNIETH.php erfolgen. Anmeldung möglich von 24.7.2017 bis 6.8.2017.
Blockkurse im 2. Semesterviertel
Von 12.10.2017 08:00 Uhr bis 3.11.2017 17:00 Uhr
NummerTitelTypECTSUmfangDozierende
551-1201-00LComputational Methods in Genome and Sequence Analysis Belegung eingeschränkt - Details anzeigen
Number of participants limited to 5.

The enrolment is done by the D-BIOL study administration.
W6 KP7GA. Wutz
KurzbeschreibungThis course aims to provide students with a comprehensive overview of computational methods for sequence analysis and assist with developing skills for application of computational approaches by experimental scientists in the life sciences.
LernzielMethods for analyzing animal genomes are increasingly becoming important for applications in human health and biotechnology suggesting that the experience will be useful to develop relevant expertise for a broad range of functions. Students will have the opportunity to advance their knowledge in programming by focusing on algorithms for genome and gene sequence analysis. A major goal of the course will be to lead the student to an independent and empowered attitude towards computational problems. For reaching this goal the students will work on an implementation of a solution for a set real-world problem in genome and sequence analysis under guided supervision.
Inhalt•Understanding the information in biological sequences and quantifying similarity
•Introduction to algorithms for sequence comparison and searches
•Implementation of sequence comparisons and searches in Python
•Accessing data formats associated with genome sequence analysis tasks
•Understanding the anatomy of a real world sequence analysis project
•Applying tools for sequence alignment and estimating error rates
•Ability to implement a solution to a problem in sequence analysis using Python
•Accessing genome annotation and retrieving relevant information in Pandas
•Application of Genomic intervals and arrays for sequence analysis with HTSeq

The course will consist of a series of lectures, assignments for implementing elementary tasks in Python, project development and discussion workshops, and 3 and a half week of practical work implementing a Pythons script as a solution to a real world problem associated with sequence analysis. At the end of the course students will explain their solutions and demonstrate the functionality of their implementations, which will then be discussed and commented on by the group. It is expected that students will be able to apply the knowledge to improve on concrete problems.
Voraussetzungen / Besonderes- It is recommended to bring your own computer with a Python installation to the course
- simple computers can be provided
- Programming basics with Python
Blockkurse im 4. Semesterviertel
Von 30.11.2017 08:00 Uhr bis 22.12.2017 17:00 Uhr
NummerTitelTypECTSUmfangDozierende
551-1417-00LIn Vivo Cryo-EM Analysis of Dynein Motor Proteins Belegung eingeschränkt - Details anzeigen
Number of participants limited to 3.

The enrolment is done by the D-BIOL study administration.
W6 KP7GT. Ishikawa
KurzbeschreibungMotor proteins convert chemical energy into mechanical motion. In this block course, we study dynein motor proteins in cilia. Dynein causes conformational change upon ATP hydrolysis and finally generate ciliary bending motion. Participants will analyze cryo-EM data of cilia and visualize in vivo 3D structure of dynein to learn how motor proteins function in the cell.
LernzielThe goal of this course is to be familiar with structural biology techniques of cryo-electron tomography and single particle cryo-EM studies on motor proteins. The main focus is 3D image analysis of cryo-EM datasets acquired by highest-end microscopes. Participants will learn structure-function relationship at various scales: how the conformational change of motor proteins causes mechanical force and generates cellular motility.
InhaltMotor proteins, such as dynein, myosin and kinesin, hydrolyze ATP to ADP and phosphate to convert chemical energy to mechanical motion. Their function is essential for intracellular transport, muscle contraction and other cellular motility as well as cell division. Motor proteins have been major targets of biophysical studies. There exist questions from atomic to tissue levels – how ATP hydrolysis causes conformational change of motor proteins; how their motion is regulated by calcium, phosphorylation and other factors; how motions of multiple motor proteins are coordinated to generate cellular motility. Structural biology has been playing central roles to answer these questions. X-ray crystallography and single particle cryo-EM address structural analysis at atomic resolution and try to reveal molecular mechanism of conformational change. Cryo-electron tomography analyze localization and 3D structure of motor proteins in the cell to explain how motions of molecular motors happen in the context of cellular environment and are integrated into cellular motion.
In this course, we study dyneins in cilia. Cilia are force-generating organelles, made by nine microtubules and thousands of dyneins. Dynein hydrolyzes ATP and undergoes conformational change, generating linear motion with respect to the microtubule. As a whole system, cilia integrate motions of these dyneins and orchestrate beating motion. To explain ciliary motion at molecular level, we need to know dynein conformational change in the cellular context. Cryo-electron tomography is recently developed technique to study molecular structures in vivo and therefore a suitable method to study dynein in cilia. Recently spatial resolution of these cryo-EM techniques was dramatically improved, driven by development of new types of detectors and electron optics.
The participants of this course will learn a program to analyze cryo-electron tomography and single particle cryo-EM data, acquired by highest-end electron microscopes and detectors in ETH and other places, and reconstruct 3D structure (tomogram) of cilia from various organisms (from green algae to human). They will further learn a program to study molecular structures from these tomograms (called subtomogram averaging) and apply it to reconstruct high-resolution 3D structure of dyneins, microtubules and regulatory proteins. This practical course is therefore mainly computational, but we will also provide students a chance of cilia preparation from green algae, cryo-EM data collection using an electron microscope in PSI and site-visit of highest-end electron microscope facility in ETH.
SkriptScripts will be distributed during the course.
LiteraturAn overview is given in the following review articles. Further literature will be indicated during the course.
Ishikawa (2017) “Axoneme structure from motile cilia” Cold Spring Harb. Perspect Biol. 9. doi: 10.1101/cshperspect.a028076.
Ishikawa (2017) “Cryo-electron tomography of motile cilia and flagella” Cilia 4, 3. doi: 10.1186/s13630-014-0012-7.
Biologie Master Information
Wahlvertiefungen
Wahlvertiefung: Ökologie und Evolution
Wahlpflicht Masterkurse
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
401-6217-00LUsing R for Data Analysis and Graphics (Part II) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions.
Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known.
LernzielThe students will be able to use the software R efficiently for data analysis.
InhaltThe course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part II of the course builds on part I and covers the following additional topics:
- Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects;
- More on R functions;
- Applying functions to elements of vectors, matrices and lists;
- Object oriented programming with R: classes and methods;
- Tayloring R: options
- Extending basic R: packages

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesBasic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course.

The course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
701-1419-00LAnalysis of Ecological DataW3 KP2GS. Güsewell
KurzbeschreibungThis class provides students with an overview of techniques for data analysis used in modern ecological research, as well as practical experience in running these analyses with R and interpreting the results. Topics include linear models, generalized linear models, mixed models, model selection and randomization methods.
LernzielStudents will be able to:
- describe the aims and principles of important techniques for the analysis of ecological data
- choose appropriate techniques for given problems and types of data
- evaluate assumptions and limitations
- implement the analyses in R
- represent the relevant results in graphs, tables and text
- interpret and evaluate the results in ecological terms
Inhalt- Linear models for experimental and observational studies
- Model selection
- Introduction to likelihood inference and Bayesian statistics
- Analysis of counts and proportions (generalised linear models)
- Models for non-linear relationships
- Grouping and correlation structures (mixed models)
- Randomisation methods
SkriptLecture notes and additional reading will be available electronically a few days before the course
LiteraturSuggested books for additional reading (available electronically)
Zuur A, Ieno EN & Smith GM (2007) Analysing ecological data. Springer, Berlin.
Zuur A, Ieno EN, Walker NJ, Saveliev AA & Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York.
Faraway JJ (2006) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis.
Voraussetzungen / BesonderesTime schedule
The course takes place on Mondays 12:45-15:00 from 25 September until 27 November, with the final exam on Monday 4 December. The last two weeks of the semester are free.

Prerequisites
- Basic statistical training (e.g. Mathematik IV in D-USYS): Data distributions, descriptive statistics, hypothesis testing, linear regression, analysis of variance
- Basic experience in data handling and data analysis in R

Individual preparation
Students without the required knowledge are asked to contact the lecturer before the first lecture date for support with individual preparation.
Wahlvertiefung: Strukturbiologie und Biophysik
Wahlpflicht Masterkurse
NummerTitelTypECTSUmfangDozierende
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
Biomedical Engineering Master Information
Vertiefungsfächer
Bioelectronics
Wahlfächer der Vertiefung
Diese Fächer sind für die Vertiefung in Bioelectronics besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Bioimaging
Kernfächer der Vertiefung
Während des Studiums müssen mindestens 12 KP aus Kernfächern einer Vertiefung (Track) erreicht werden.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Wahlfächer der Vertiefung
Diese Fächer sind für die Vertiefung in Bioimaging besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser.
NummerTitelTypECTSUmfangDozierende
227-0391-00LMedical Image Analysis
Findet dieses Semester nicht statt.
W3 KP2GE. Konukoglu
KurzbeschreibungIt is the objective of this lecture to introduce the basic concepts used
in Medical Image Analysis. In particular the lecture focuses on shape
representation schemes, segmentation techniques, and the various image registration methods commonly used in Medical Image Analysis applications.
LernzielThis lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
Voraussetzungen / BesonderesBasic knowledge of computer vision would be helpful.
227-0969-00LMethods & Models for fMRI Data Analysis Information W6 KP4VK. Stephan
KurzbeschreibungThis course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data.
LernzielTo obtain in-depth knowledge of the theoretical foundations of SPM
and DCM and of their application to empirical fMRI data.
InhaltThis course teaches state-of-the-art methods and models for fMRI data analysis. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of studies in psychiatry, neurology and neuroeconomics.
Biomechanics
Kernfächer der Vertiefung
Während des Studiums müssen mindestens 12 KP aus Kernfächern einer Vertiefung (Track) erreicht werden.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Medical Physics
Weitere Wahlfächer
Diese Fächer können für die Vertiefung in Medical Physics geeignet sein. Bitte konsultieren Sie Ihren Track Adviser.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Biotechnologie Master Information
Master-Studium (Studienreglement 2017)
Praktika
Students need to acquire a total of 14 ECTS in lab courses.
All listed lab courses are mandatory.
NummerTitelTypECTSUmfangDozierende
636-0201-00LLab Course: Methods in Cell Analysis and Laboratory Automation Belegung eingeschränkt - Details anzeigen
Only for Biotechnology MSc, Programme Regulations 2017.
O2 KP6PT. Horn
KurzbeschreibungThe course Methods in Cell Analysis and Laboratory Automation introduces students to high-end cell analysis and sample preparation methods including image analysis. Students will be taught theoretical aspects and skills in Flow Cytometry, Light Microscopy, Image Analysis, and the use of Laboratory Automation.
Lernziel-to understand the technical and physical principles of light microscopes and flow cytometers
-to have hands-on experience in the use of these technologies to analyze/image real samples
-to be able to run a basic analysis of the data and images obtained with flow cytometers and microscopes
-to get introduced to liquid handling (pipetting) robotics and learn how to implement a basic workflow
InhaltThe practical course will have five units at 2 days each (total 10 days):
1. Flow Cytometry:
a. Introduction to Flow Cytometry
b. Practical demonstration on flow cytometry analyzers and flow cytometry cell sorters
c. Flow cytometry sample preparation
d. Learn how to use flow cytometry equipment to analyze and sort fluorescence-labeled cells
2. Light microscopy
a. Learn how to build a microscope and understand the underlying physical principles
b. Learn how to use a modern automated wide field fluorescence microscope
c. Use this microscope to automatically acquire images of a cell culture assay to analyze the dose-dependent effect of a drug treatment
3. Image Analysis
a. Introduction to the fundamentals of image analysis
b. Learn the basics of the image analysis software Fiji/ImageJ
c. Use Fiji/ImageJ to analyze the images acquired during the microscopy exercise
4. Laboratory Automation
a. Introduction to the basics of automated liquid handling/ lab robotics
b. See examples on using lab automation for plasmid library generation and cell cultivation
c. Learn how to program and execute a basic pipetting workflow including liquid handling and labware transfers on Tecan and Hamilton robotic systems
5. Presentations
a. Each student will be assigned to an individual topic of the course and will have to prepare a presentation on it.
b. Presentations and discussion in form of a Colloquium
SkriptYou will find further information on the practical course and the equipment at:
https://www.bsse.ethz.ch/scf
https://www.bsse.ethz.ch/laf
LiteraturMicroscopy: Murphy and Davidson, Fundamentals of Light Microscopy and Electronic Imaging, John Wiley & Sons, 2012
Flow Cytometry: Shapiro, Practical Flow Cytometry, John Wiley & Sons, 2005
Image analysis: R. C. Gonzalez, R. E. Woods, Digital Image Processing (3rd Edition), Prentice Hall
Laboratory Automation: Design and construction of a first-generation high-throughput integrated robotic molecular biology platform for bioenergy applications (2011) J. Lab. Autom., 16(4), 292-307
Voraussetzungen / BesonderesThe following knowledge is required for the course:
-basic laboratory methods
-basic physics of optics (properties of light, refraction, lenses, fluorescence)
-basic biology of cells (cell anatomy and physiology)
CAS in Angewandter Statistik Information
Kursdauer: ca. 12 Monate

Nächster Kursbeginn im FS 2019
Obligatorische Fächer
NummerTitelTypECTSUmfangDozierende
447-0625-01LApplied Analysis of Variance and Experimental Design I Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
O3 KP1V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Weitere Fächer
NummerTitelTypECTSUmfangDozierende
447-0625-02LApplied Analysis of Variance and Experimental Design II Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
Z3 KP1V + 1UL. Meier
KurzbeschreibungRandom effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze sophisticated experiments in the fields of natural sciences. They will gain practical experience by using the software R.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
CAS in Entwicklung und Zusammenarbeit Information
Module
NummerTitelTypECTSUmfangDozierende
865-0000-06LWirkungsanalysen: Methoden und Anwendungen
Nur für Studierende des MAS bzw. CAS in Entwicklung und Zusammenarbeit sowie Fachkräfte mit mind. 24 Monaten Berufserfahrung in der internationalen Zusammenarbeit.
Doktoranden, die sich mit empirischer Forschung im EZA-Bereich befassen, können "sur Dossier" zugelassen werden.

Einschreibung nur über das NADEL-Sekretariat.
W2 KP3GI. Günther
KurzbeschreibungDie Veranstaltung bietet einen Überblick über verschiedene Methoden, die für eine aussagekräftige und fundierte Analyse der Auswirkungen von Entwicklungsprogrammen und -projekten herangezogen werden können. Die Veranstaltung vermittelt sowohl grundlegende Methodenkenntnisse als auch Praxisbeispiele aus der Entwicklungszusammenarbeit von bi- und multilateralen Gebern und NGOs.
LernzielDie Teilnehmer kennen die wichtigsten Methoden für rigorose Wirkungsanalysen und sind in der Lage, für bestehende Programme und Projekte der Entwicklungszusammenarbeit kleinere Wirkungsanalysen selbst durchzuführen und umfangreiche Wirkungsanalysen in Auftrag zu geben und zu verfolgen. Des Weiteren können Teilnehmer die Ergebnisse eigener und externer Wirkungsanalysen effektiv nutzen.
InhaltEinführung in rigorose Wirkungsanalysen; Anwendungsbereiche und Beispiele; Vermittlung grundlegender statistischer Kenntnisse für Wirkungsanalysen; Vor- und Nachteile quantitativer Analysen; Experimentelle und quasi-experimentelle Methoden; Auswahl geeigneter Indikatoren; Vollstaendige Wirkungsketten; Datenerhebung, -management und -analyse; Projektmanagement einer Wirkungsanalyse.
Voraussetzungen / BesonderesDer Besuch der Lehrveranstaltung ist an Voraussetzungen gebunden, die der Homepage des NADEL zu entnehmen sind. Elektronische Einschreibung darf erst nach Einschreibung am NADEL-Sekretariat erfolgen.
Chemie Bachelor Information
1. Semester
Obligatorische Fächer Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0271-00LGrundlagen der Mathematik I (Analysis A)O5 KP3V + 2UL. Keller
KurzbeschreibungAnwendungsorientierte Einführung in die eindimensionale Analysis. Einfache Modelle kennen, selber bilden und mathematisch analysieren können.
Funktionen einer Variablen: Funktionsbegriff, Ableitungsbegriff, die Idee der Differentialgleichung, komplexe Zahlen, Taylorpolynome und Taylorreihen. Integrale von Funktionen einer Variablen.
LernzielGrundlegende Begriffe der eindimensionalen Analysis kennen und mit ihnen umgehen können. Einfache Modelle kennen oder selber bilden und mathematisch analysieren.
InhaltFunktionen einer Variablen: Funktionsbegriff, Ableitungsbegriff, die Idee der Differentialgleichung, komplexe Zahlen, Taylorpolynome und Taylorreihen. Integrale von Funktionen einer Variablen.
LiteraturG. B. Thomas, M. D. Weir, J. Hass: Analysis 1, Lehr- und Übungsbuch, Pearson-Verlag
D. W. Jordan, P. Smith: Mathematische Methoden für die Praxis, Spektrum Akademischer Verlag
R. Sperb/M. Akveld: Analysis I (vdf)
L. Papula: Mathematik für Ingenieure und Naturwissenschaftler (3 Bände), Vieweg
weitere Literatur wird in der Vorlesung angegeben
Chemieingenieurwissenschaften Bachelor Information
1. Semester
Obligatorische Fächer Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0271-00LGrundlagen der Mathematik I (Analysis A)O5 KP3V + 2UL. Keller
KurzbeschreibungAnwendungsorientierte Einführung in die eindimensionale Analysis. Einfache Modelle kennen, selber bilden und mathematisch analysieren können.
Funktionen einer Variablen: Funktionsbegriff, Ableitungsbegriff, die Idee der Differentialgleichung, komplexe Zahlen, Taylorpolynome und Taylorreihen. Integrale von Funktionen einer Variablen.
LernzielGrundlegende Begriffe der eindimensionalen Analysis kennen und mit ihnen umgehen können. Einfache Modelle kennen oder selber bilden und mathematisch analysieren.
InhaltFunktionen einer Variablen: Funktionsbegriff, Ableitungsbegriff, die Idee der Differentialgleichung, komplexe Zahlen, Taylorpolynome und Taylorreihen. Integrale von Funktionen einer Variablen.
LiteraturG. B. Thomas, M. D. Weir, J. Hass: Analysis 1, Lehr- und Übungsbuch, Pearson-Verlag
D. W. Jordan, P. Smith: Mathematische Methoden für die Praxis, Spektrum Akademischer Verlag
R. Sperb/M. Akveld: Analysis I (vdf)
L. Papula: Mathematik für Ingenieure und Naturwissenschaftler (3 Bände), Vieweg
weitere Literatur wird in der Vorlesung angegeben
Computational Biology and Bioinformatics Master Information
More informations at: https://www.cbb.ethz.ch/
Master-Studium (Studienreglement 2017)
Kernfächer
Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed.
Students need to pass at least one course in each core subcategory.
A total of 40 ECTS needs to be acquired in the core course category.
Data Science
NummerTitelTypECTSUmfangDozierende
401-6282-00LStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: STA426

Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html
W5 KP3GH. Rehrauer, M. Robinson
KurzbeschreibungA range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments.
Lernziel-Understand the fundamental "scientific process" in the field of Statistical Bioinformatics
-Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave)
-Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data
-Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data
-Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project
-Gain the ability to critical assess the statistical bioinformatics literature
-Write a coherent summary of a bioinformatics problem and its solution in statistical terms
InhaltLectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification
SkriptLecture notes, published manuscripts
Voraussetzungen / BesonderesPrerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics

Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data
Master-Studium (Studienreglement 2011)
Kernfächer
NummerTitelTypECTSUmfangDozierende
401-6282-00LStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: STA426

Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html
W5 KP3GH. Rehrauer, M. Robinson
KurzbeschreibungA range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments.
Lernziel-Understand the fundamental "scientific process" in the field of Statistical Bioinformatics
-Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave)
-Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data
-Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data
-Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project
-Gain the ability to critical assess the statistical bioinformatics literature
-Write a coherent summary of a bioinformatics problem and its solution in statistical terms
InhaltLectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification
SkriptLecture notes, published manuscripts
Voraussetzungen / BesonderesPrerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics

Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data
DAS in Angewandter Statistik Information
Kursdauer: ca. 20 Monate.

Nächster Kursbeginn im FS 2019
Obligatorische Fächer
NummerTitelTypECTSUmfangDozierende
447-0625-01LApplied Analysis of Variance and Experimental Design I Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
O3 KP1V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
447-0625-02LApplied Analysis of Variance and Experimental Design II Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
W3 KP1V + 1UL. Meier
KurzbeschreibungRandom effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze sophisticated experiments in the fields of natural sciences. They will gain practical experience by using the software R.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Data Science Master Information
Kernfächer
Wählbare Kernfächer
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Doktorat Departement Chemie und Angewandte Biowissenschaften Information
Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html
Lehrangebot Doktorat und Postdoktorat
Doktoratsausbildung in anorganischer Chemie
NummerTitelTypECTSUmfangDozierende
529-0169-00LInstrumental AnalysisE-0 KP2SD. Günther
KurzbeschreibungGroup seminar on elemental analysis and isotope ratio determinations using various plasma sources
Lernziel
InhaltDevelopments in plasma mass spectrometry and alternative plasma sources
Doktorat Departement Maschinenbau und Verfahrenstechnik Information
Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html
Lehrangebot Doktorat und Postdoktorat
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Doktorat Departement Mathematik Information
Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html

Die Liste der Lehrveranstaltungen (samt der zugehörigen Anzahl Kreditpunkte) für Doktoratsstudentinnen und Doktoratsstudenten wird jedes Semester im Newsletter der ZGSM veröffentlicht.
www.zgsm.ch/index.php?id=260&type=2
ACHTUNG: Kreditpunkte fürs Doktoratsstudium sind nicht mit ECTS-Kreditpunkten zu verwechseln!
Graduate School / Graduiertenkolleg
Offizielle Website der Zurich Graduate School in Mathematics:
www.zurich-graduate-school-math.ch
NummerTitelTypECTSUmfangDozierende
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UA. Jentzen
KurzbeschreibungCourse on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.
LernzielThe aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.
InhaltGeneration of random numbers
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
SkriptLecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).
LiteraturP. Glassermann:
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.

P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
Voraussetzungen / BesonderesPrerequisites:

Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.

a) mandatory courses:
Elementary Probability,
Probability Theory I.

b) recommended courses:
Stochastic Processes.

Start of lectures: Wednesday, September 20, 2017

Date of the End-of-Semester examination: Wednesday, December 20, 2017, 13:00-15:00; students must arrive before 12:30 at ETH HG E 19.
Room for the End-of-Semester examination: ETH HG E 19.

Exam inspection: Monday, March 5, 2018,
13:00-14:00 at HG D 5.1
Please bring your legi.
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
Kolloquien
NummerTitelTypECTSUmfangDozierende
401-5350-00LAnalysis Seminar Information E-0 KP1KM. Struwe, A. Carlotto, F. Da Lio, A. Figalli, N. Hungerbühler, T. Ilmanen, T. Kappeler, T. Rivière, D. A. Salamon
KurzbeschreibungResearch colloquium
Lernziel
Doktorat Departement Umweltsystemwissenschaften Information
Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html
Umweltwissenschaften
Atmosphäre und Klima
NummerTitelTypECTSUmfangDozierende
701-1253-00LAnalysis of Climate and Weather Data Information W3 KP2GC. Frei
KurzbeschreibungObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
LernzielObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
InhaltIntroduction into the theoretical background and the practical application of methods of data analysis in meteorology and climatology.

Topics: exploratory methods, hypothesis testing, analysis of climate trends, measuring the skill of climate and forecasting models, analysis of extremes, principal component analysis and maximum covariance analysis.

The lecture also provides an introduction into R, a programming language and graphics tool frequently used for data analysis in meteorology and climatology. During hands-on computer exercises the student will become familiar with the practical application of the methods.
SkriptDocumentation and supporting material include:
- documented view graphs used during the lecture
- excercise sets and solutions
- R-packages with software and example datasets for exercise sessions

All material is made available via the lecture web-page.
LiteraturSuggested literature:
- Wilks D.S., 2005: Statistical Methods in the Atmospheric Science. (2nd edition). International Geophysical Series, Academic Press Inc. (London)
- Coles S., 2001: An introduction to statistical modeling of extreme values. Springer, London. 208 pp.
Voraussetzungen / BesonderesPrerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren.
Elektrotechnik und Informationstechnologie Bachelor Information
1. Semester
Fächer der Basisprüfung
Basisprüfungsblock B
NummerTitelTypECTSUmfangDozierende
401-0231-10LAnalysis IO8 KP4V + 3UT. H. Willwacher
KurzbeschreibungReelle und komplexe Zahlen, Vektoren, Grenzwerte, Folgen, Reihen, Potenzreihen, stetige Abbildungen, Differential- und Integralrechnung einer Variablen, Einführung in gewöhnliche Differentialgleichungen
LernzielEinfuehrung in die Grundlagen der Analysis
SkriptKonrad Koenigsberger, Analysis I.
Christian Blatter: Ingenieur-Analysis (Kapitel 1-3)
3. Semester
Prüfungsblöcke
Prüfungsblock 1
NummerTitelTypECTSUmfangDozierende
401-0353-00LAnalysis III Information O4 KP2V + 1UA. Figalli
KurzbeschreibungIn dieser Lehrveranstaltung werden Probleme der angewandten Analysis behandelt, speziell ausgerichtet auf die Bedürfnisse der Elektrotechniker. Dazu gehört vor allem das Studium der einfachsten Fälle der drei Grundtypen von partiellen Differentialgleichungen zweiten Grades: Laplace-Gleichung, Wärmeleitungsgleichung und Wellengleichung.
Lernziel
Inhalt1.) Klassifizierung von PDE's
- linear, quasilinear, nicht-linear
- elliptisch, parabolisch, hyperbolisch

2.) Quasilineare PDE
- Methode der Charakteristiken (Beispiele)

3.) Elliptische PDE
- Bsp: Laplace-Gleichung
- Harmonische Funktionen, Maximumsprinzip, Mittelwerts-Formel.
- Methode der Variablenseparation.

4.) Parabolische PDE
- Bsp: Wärmeleitungsgleichung
- Bsp: Inverse Wärmeleitungsgleichung
- Methode der Variablenseparation

5.) Hyperbolische PDE
- Bsp: Wellengleichung
- Formel von d'Alembert in (1+1)-Dimensionen
- Methode der Variablenseparation

6.) Green'sche Funktionen
- Rechnen mit der Dirac-Deltafunktion
- Idee der Green'schen Funktionen (Beispiele)

7.) Ausblick auf numerische Methoden
- 5-Punkt-Diskretisierung des Laplace-Operators (Beispiele)
LiteraturY. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005)

Zusätzliche Literatur:
Erwin Kreyszig, "Advanced Engineering Mathematics", John Wiley & Sons, Kap. 8, 11, 16 (sehr gutes Buch, als Referenz zu benutzen)
Norbert Hungerbühler, "Einführung in die partiellen Differentialgleichungen", vdf Hochschulverlag AG an der ETH Zürich.
G. Felder:Partielle Differenzialgleichungen.
https://people.math.ethz.ch/~felder/PDG/
Voraussetzungen / BesonderesVoraussetzungen: Analysis I und II, Fourier Reihen (Komplexe Analysis)
Elektrotechnik und Informationstechnologie Master Information
Fächer der Vertiefung
Insgesamt 42 KP müssen im Masterstudium aus Vertiefungsfächern erreicht werden. Der individuelle Studienplan unterliegt der Zustimmung eines Tutors.
Communication
Empfohlene Fächer
Diese Fächer sind eine Empfehlung. Sie können Fächer aus allen Vertiefungsrichtungen wählen. Sprechen Sie mit Ihrem Tutor.
NummerTitelTypECTSUmfangDozierende
227-0377-00LPhysics of Failure and Failure Analysis of Electronic Devices and EquipmentW3 KP2VU. Sennhauser
KurzbeschreibungFailures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
LernzielIntroduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
SkriptComprehensive copy of transparencies
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Computers and Networks
Empfohlene Fächer
Diese Fächer sind eine Empfehlung. Sie können Fächer aus allen Vertiefungsrichtungen wählen. Sprechen Sie mit Ihrem Tutor.
NummerTitelTypECTSUmfangDozierende
227-0377-00LPhysics of Failure and Failure Analysis of Electronic Devices and EquipmentW3 KP2VU. Sennhauser
KurzbeschreibungFailures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
LernzielIntroduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
SkriptComprehensive copy of transparencies
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Electronics and Photonics
Empfohlene Fächer
Diese Fächer sind eine Empfehlung. Sie können Fächer aus allen Vertiefungsrichtungen wählen. Sprechen Sie mit Ihrem Tutor.
NummerTitelTypECTSUmfangDozierende
227-0377-00LPhysics of Failure and Failure Analysis of Electronic Devices and EquipmentW3 KP2VU. Sennhauser
KurzbeschreibungFailures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
LernzielIntroduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
SkriptComprehensive copy of transparencies
Energy and Power Electronics
Kernfächer
Diese Fächer sind besonders empfohlen, um sich in "Energy and Power Electronics" zu vertiefen.
NummerTitelTypECTSUmfangDozierende
227-0526-00LPower System Analysis Information W6 KP4GG. Hug
KurzbeschreibungZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
LernzielZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes.
InhaltDer Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert.
SkriptVorlesungsskript.
Systems and Control
Empfohlene Fächer
Diese Fächer sind eine Empfehlung. Sie können Fächer aus allen Vertiefungsrichtungen wählen. Sprechen Sie mit Ihrem Tutor.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
227-0526-00LPower System Analysis Information W6 KP4GG. Hug
KurzbeschreibungZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
LernzielZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes.
InhaltDer Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert.
SkriptVorlesungsskript.
Signal Processing and Machine Learning
Coming soon!
Kernfächer
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Fächer von allgemeinem Interesse
NummerTitelTypECTSUmfangDozierende
227-0377-00LPhysics of Failure and Failure Analysis of Electronic Devices and EquipmentW3 KP2VU. Sennhauser
KurzbeschreibungFailures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
LernzielIntroduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
SkriptComprehensive copy of transparencies
Energy Science and Technology Master Information
Kernfächer
Obligatorische Kernfächer
NummerTitelTypECTSUmfangDozierende
227-1631-00LEnergy System Analysis Information W4 KP3GG. Hug, S. Hellweg, F. Noembrini, A. Schlüter
KurzbeschreibungThe course provides an introduction to the methods and tools for analysis of energy consumption, energy production and energy flows. Environmental aspects are included as well as economical considerations. Different sectors of the society are discussed, such as electric power, buildings, and transportation. Models for energy system analysis planning are introduced.
LernzielThe purpose of the course is to give the participants an overview of the methods and tools used for energy systems analysis and how to use these in simple practical examples.
InhaltThe course gives an introduction to methods and tools for analysis of energy consumption, energy production and energy flows. Both larger systems, e.g. countries, and smaller systems, e.g. industries, homes, vehicles, are studied. The tools and methods are applied to various problems during the exercises. Different conventions of energy statistics used are introduced.

The course provides also an introduction to energy systems models for developing scenarios of future energy consumption and production. Bottom-up and Top-Down approaches are addressed and their features and applications discussed.

The course contains the following parts:
Part I: Energy flows and energy statistics
Part II: Environmental impacts
Part III: Electric power systems
Part IV: Energy in buildings
Part V: Energy in transportation
Part VI: Energy systems models
SkriptHandouts
LiteraturExcerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8
Wählbare Kernfächer
These courses are particularly recommended, other ETH-courses from the field of Energy Science and Technology at large may be chosen in accordance with your tutor.
NummerTitelTypECTSUmfangDozierende
227-0526-00LPower System Analysis Information W6 KP4GG. Hug
KurzbeschreibungZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
LernzielZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes.
InhaltDer Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert.
SkriptVorlesungsskript.
Weitere Wahlfächer
NummerTitelTypECTSUmfangDozierende
151-0360-00LMethoden der StrukturanalyseW4 KP2V + 1UG. Kress
KurzbeschreibungDie Grundlagen der Strukturauslegung werden nach den Kriterien der Festigkeit, der Stabilität, der Ermüdungsauslegung und der elasto-plastischen Strukturanalyse behandelt.
Strukturtheorien (für eindimensionalen und zweidimensionalen Tragwerke) werden auf der Basis der Energie sätze präsentiert.
LernzielErweiterung der Grundlagen zur Behandlung strukturmechanischer Auslegungsproblemen. Einführung in die Dimensionierung von Flächentragwerke. Verständnis des Zusammenhangs zwischen Materialverhalten, Strukturtheorien und Auslegungskriterien.
Inhalt1. Grundproblem der Kontinuumsmechanik und Energiesätze: Herleitung von Strukturtheorien; Homogenisierungstheorien; Finite Elementen; Bruchmechanik.
2. Strukturtheorien für Flächentragwerke und Stabilität: Scheiben, Platten; Beulen von Platten (nichtlineare Plattentheorie)
3. Festigkeitshypothesen und Materialverhalten: Duktiles Verhalten, Plastizität, vMises, Tresca, Hauptspannungshypothese; Sprödes Verhalten; Viskoplastisches Verhalten, Kriechfestigkeit
4. Strukturauslegung: Ermüdung und dynamische Strukturanalyse
SkriptSkript und alle anderen Vorlesungsunterlagen erhältlich auf MOODLE
Voraussetzungen / Besondereskeine
Erdwissenschaften Bachelor Information
Bachelor-Studium (Studienreglement 2016)
1. Semester
Fächer der Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0251-00LMathematik I: Analysis I und Lineare AlgebraO6 KP4V + 2UL. Halbeisen
KurzbeschreibungDiese Vorlesung behandelt mathematische Konzepte und Methoden, die zum Modellieren, Lösen und Diskutieren wissenschaftlicher Probleme nötig sind - speziell durch gewöhnliche Differentialgleichungen.
LernzielMathematik ist von immer grösserer Bedeutung in den Natur- und Ingenieurwissenschaften. Grund dafür ist das folgende Konzept zur Lösung konkreter Probleme: Der entsprechende Ausschnitt der Wirklichkeit wird in der Sprache der Mathematik modelliert; im mathematischen Modell wird das Problem - oft unter Anwendung von äusserst effizienter Software - gelöst und das Resultat in die Realität zurück übersetzt.

Ziel der Vorlesungen Mathematik I und II ist es, die einschlägigen mathematischen Grundlagen bereit zu stellen. Differentialgleichungen sind das weitaus wichtigste Hilfsmittel im Prozess des Modellierens und stehen deshalb im Zentrum beider Vorlesungen.
Inhalt1. Differential- und Integralrechnung:
Wiederholung der Ableitung, Linearisierung, Taylor-Polynome, Extremwerte, Stammfunktion, Hauptsatz der Differential- und Integralrechnung, Integrationsmethoden, uneigentliche Integrale.

2. Lineare Algebra und Komplexe Zahlen:
lineare Gleichungssysteme, Gauss-Verfahren, Matrizen, Determinanten, Eigenwerte und Eigenvektoren, Darstellungsformen der komplexe Zahlen, Potenzieren, Radizieren, Fundamentalsatz der Algebra.

3. Gewöhnliche Differentialgleichungen:
Separierbare Differentialgleichungen (DGL), Integration durch Substitution, Lineare DGL erster und zweiter Ordnung, homogene Systeme linearer DGL mit konstanten Koeffizienten, Einführung in die dynamischen Systeme in der Ebene.
Literatur- Thomas, G. B., Weir, M. D. und Hass, J.: Analysis 1, Lehr- und Übungsbuch (Pearson).
- Gramlich, G.: Lineare Algebra, eine Einführung (Hanser).
- Papula, L.: Mathematik für Ingenieure und Naturwissenschaftler, Bd. 1 und 2 (Vieweg+Teubner).
Voraussetzungen / BesonderesVoraussetzungen: Vertrautheit mit den Grundlagen der Analysis, insbesondere mit dem Funktions- und Ableitungsbegriff.

Mathe-Lab (Präsenzstunden):
Mo 12-14, Di 17-19, Mi 17-19, stets im Raum HG E 41.
Allgemeine erdwissenschaftliche Fächer
NummerTitelTypECTSUmfangDozierende
651-4271-00LErdwissenschaftliche Datenanalyse und Visualisierung mit MatlabO3 KP3GS. Wiemer, G. De Souza, T. Tormann
KurzbeschreibungDie Vorlesung und dazugehörige Übung geben den Studierenden eine Einführung in die Konzepte und Werkzeuge der wissenschaftlichen Datenanalyse. Anhand von praktischen erdwissenschaftlichen Problemstellungen werden in Kleingruppen und Einzelarbeit Aufgaben von wachsender Komplexität mit der Software MATLAB gelöst. Dabei lernen die Studierenden auch, Datensätze effektvoll zu visualisieren.
LernzielDie folgenden Konzepte werden vorgestellt:
- Effektvolle Datenanalyse und Visualisierung in 2D und 3D
- Arbeiten mit Matrizen und Arrays
- Programmieren und Algorithmenentwicklung
- Animationen sinnvoll einsetzen
- Einen Datensatz statistisch erfassen
- Interaktives Datamining
- Unsicherheiten, Fehlerfortpflanzung und Bootstrapping
- Regressionsanalysen
- Testen von Hypothesen
3. Semester
Grundlagenfächer II
Prüfungsblock 2
NummerTitelTypECTSUmfangDozierende
701-0071-00LMathematik III: SystemanalyseO4 KP2V + 1UN. Gruber, M. Vogt
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
Bachelor-Studium (Studienreglement 2010)
5. Semester Vertiefungen
Vertiefung Klima und Wasser
Für Beratungen in der Vertiefung Klima und Wasser steht Dr. Erich Fischer, Institut für Klima und Atmosphäre, zur Verfügung
Wahlfächer der Vertiefung Klima und Wasser
Neben dem obligatorischen Seminar für Bachelorstudierende: Atmosphäre und Klima (Lerneinheit Nr. 701-0459-00 im Herbstsemester) müssen 22 KP aus dem unter "Wahlfächern" aufgeführten Angebot des 5. und 6. Semesters erworben werden. Die Wahl anderer Fächer ist mit dem Fachberater (Dr. Erich Fischer) abzusprechen.
NummerTitelTypECTSUmfangDozierende
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
Erdwissenschaften Master Information
Vertiefung in Geology
Pflichtmodul Analytical Methods in Earth Sciences
Es sind je 6KP innerhalb dem Teil A und 6KP innerhalb dem Teil B zu belegen.
Teil B: Methoden
NummerTitelTypECTSUmfangDozierende
651-4117-00LSediment AnalysisW+3 KP2GM. G. Fellin, A. Gilli, V. Picotti
KurzbeschreibungTheoretische Grundlagen und Anwendungen von einfachen Methoden der Sedimentuntersuchung.
LernzielDas Ziel ist die korrekte Anwendung der Korngrösse- und Gefüge-Analyse an Sedimenten, um die sedimentären Prozesse und Ablagerungsräume zu bestimmen.
Wahlmodule Geology
Basin Analysis
Basin Analysis: Obligatorische Fächer
NummerTitelTypECTSUmfangDozierende
651-4231-00LBasin AnalysisW+3 KP2GS. Willett, T. I. Eglinton, M. Lupker
KurzbeschreibungThe course discusses the formation and development of different basin types as part of lithosphere geodynamics. It introduces conceptual models and governing physics, with practical application to the study of basin evolution. Techniques for the analysis of subsidence and thermal history are demonstrated. Organic matter, petroleum play, and their biogeochemical investigation are examined.
LernzielBased on the introductory education and practical training during this course, each participant should be able to choose and apply approaches and techniques to own problems of basin analysis, and should be versed to expand their knowledge independently.

In particular, each participant should:

- Develop an intuitive understanding for origin, dynamics, and temporal evolution of basins in a geological / geodynamic context;

- Acquire the necessary theoretical foundation to describe basin evolution quantitatively;

- Be familiar with geological and geophysical methods that are applied to obtain information about rock properties, structural geometry, and thermal and subsidence history of basins;

- Understand the burial and maturation of organic matter in basins, the development of petroleum play, and be acquainted with geochemical methods to study the evolution of biogenic carbon.
InhaltThe following topics are covered:

- Introduction; classification schemes and types of basins; heat conduction; geotherms;

- The lithosphere; isostasy; rifts and basins due to lithospheric stretching; uniform extension model; modifications to the uniform stretching model; dynamics of rifting.

- Elasticity of the lithosphere; flexural compensation; geometry and analytical description of loads and the resulting deflection; foreland basins; their anatomy;

- Reconstruction of basin evolution; borehole data; porosity loss and decompaction; backstripping; subsidence curves; thermal history and its reconstruction;

- Petroleum play concept; organic production; source rock prediction and depositional environment; petroleum generation, expulsion, migration, alteration; reservoir and traps;

- Carbon cycle; maturation of organic matter; geochemistry of biogenic carbon; biomarkers; analytical techniques

- Overview of other basin types: effects of mantle dynamics, strike-slip basins.

Each week of the course is split in lectures and corresponding practicals, in which the concepts are applied to simplified problems.

Grading of the semester performance is based on submitted practicals (50%) and a final exam (50%). The exam will take place in the time slot of the last practical (18.12.).
SkriptLecture notes are provided online during the course. They summarize the current subjects week by week, and provide the essential theoretical background.
LiteraturMain reference :

Allen, P.A., and Allen, J.R., 2013. Basin Analysis - Principles and Application to petroleum play assessment
3rd edition, 619 pp. Wiley-Blackwell, Chichester, UK.
ISBN 978-0-470-67376-8

Recommended, but not required (available in library).



Supplementary:
Turcotte, D.L., and Schubert, S., 2002. Geodynamics.
2nd edition, 456 pp. Cambridge University Press.
ISBN 0-521-66624-4.

Peters, K.E., Walters, C.C., Moldowan, J.M., 2005. The biomarker guide (volume 2).
2nd edition, Cambridge University Press.
ISBN 0-521-83762-6.
Voraussetzungen / BesonderesFamiliarity with MATLAB is advantageous, but not required.
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
406-0243-AALAnalysis I and II Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-14 KP30RM. Akka Ginosar
KurzbeschreibungMathematical tools for the engineer
LernzielMathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers.
InhaltComplex numbers.
Calculus for functions of one variable with applications.
Simple Mathematical models in engineering.

Multi variable calculus: gradient, directional derivative, chain rule, Taylor expansion, Lagrange multipliers. Multiple integrals: coordinate transformations, path integrals, integrals over surfaces, divergence theorem, applications in physics. Ordinary differential equations.
LiteraturTextbooks in English:
- J. Stewart: Calculus, Cengage Learning, 2009, ISBN 978-0-538-73365-6.
- J. Stewart: Multivariable Calculus, Thomson Brooks/Cole.
- V. I. Smirnov: A course of higher mathematics. Vol. II. Advanced calculus.
- W. L. Briggs, L. Cochran: Calculus: Early Transcendentals: International Edition, Pearson Education. ISBN 978-0-321-65193-8.
Textbooks in German:
- M. Akveld, R. Sperb: Analysis I, vdf
- M. Akveld, R. Sperb: Analysis II, vdf
- L. Papula: Mathematik für Ingenieure und Naturwissenschaftler, Vieweg Verlag
- L. Papula: Mathematik für Ingenieure 2, Vieweg Verlag
Geomatik und Planung Bachelor Information
1. Semester
Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0241-00LAnalysis I Information O7 KP5V + 2UM. Akka Ginosar
KurzbeschreibungMathematische Hilfsmittel des Ingenieurs
LernzielMathematik als Hilfsmittel zur Lösung von Ingenieurproblemen:
Verständnis für mathematische Formulierung von technischen und naturwissenschaftlichen Problemen.
Erarbeitung des mathematischen Grundwissens für einen Ingenieur.
InhaltKomplexe Zahlen.
Differentialrechnung und Integralrechnung für Funktionen einer Variablen mit Anwendungen.
Einfache mathematische Modelle in den Naturwissenschaften.
SkriptDie Vorlesung folgt weitgehend

Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer; online verfügbar unter:
http://link.springer.com/book/10.1007/978-3-8348-2559-9/page/1
LiteraturNeben Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer sind auch die folgenden Bücher/Skripte empfehlenswert und decken den zu behandelnden Stoff ab:

Tilo Arens et al., "Mathematik", Springer; online verfügbar unter:
http://link.springer.com/book/10.1007/978-3-642-44919-2/page/1

Meike Akveld, "Analysis 1", vdf;
http://vdf.ch/index.php?route=product/product&product_id=1706

Urs Stammbach, "Analysis I/II" (erhältlich im ETH Store);
https://people.math.ethz.ch/~stammb/analysisskript.html
Geomatik Master Information
Wahlfächer
Den Studierenden steht das gesamte Lehrangebot der ETHZ und der Universität Zürich zur individuellen Auswahl offen.
Empfohlene Wahlfächer des Studiengangs
NummerTitelTypECTSUmfangDozierende
101-0439-00LIntroduction to Economic Analysis - A Case Study Approach with Cost Benefit Analysis in Transport
Remark:
Former Title "Introduction to Economic Policy - A Case Study Approach with Cost Benefit Analysis in Transport".
W6 KP4GK. W. Axhausen, R. Schubert
KurzbeschreibungDie Vorlesung stellt einige grundlegende ökonomische Prinzipien sowie die Verfahren der Kosten-Nutzen-Analyse vor; sie führt auch in Methoden zur Ermittlung von Bewertungsgrössen ein
LernzielSichere Kenntnis mikro- und makroökonomischer Grundlagen. Erarbeitung und Übung von Verfahren der Bewertung von Massnahmen und infrastrukturellen Ausbauten
InhaltMikro-und makroökonomische Grundlagen; Kosten - Nutzen - Analyse; Nutzwertanalyse; Europäische Richtlinien; Stated response Verfahren; Reisekostenansatz et al.; Bewertung von Reisezeitveränderungen; Bewertung der Verkehrssicherheit
Skriptmoodle Plattform für die ökonomischen Grundlagen; Umdrucke
LiteraturTaylor, M.P., Mankiw, N.G. (2014): Economics; Harvard Press

VSS (2006) SN 640 820: Kosten-Nutzen-Analysen im Strassenverkehr, VSS, Zürich.

Boardman, A.E., D.H. Greenberg, A.R. Vining und D.L. Weimer (2001) Cost – Benefit – Analysis: Concepts and Practise, Prentice-Hall, Upper Saddle River.

ecoplan and metron (2005) Kosten-Nutzen-Analysen im Strassenverkehr: Kommentar zu SN 640 820, UVEK, Bern.
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
103-0255-AALGeodata Analysis
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-2 KP4RM. Raubal
KurzbeschreibungThe course deals with advanced methods in spatial data analysis.
Lernziel- Understanding the theoretical principles in spatial data analysis.
- Understanding and using methods for spatial data analysis.
- Detecting common sources of errors in spatial data analysis.
- Advanced practical knowledge in using appropriate GIS-tools.
InhaltThe course deals with advanced methods in spatial data analysis in theory as well as in practical exercises.
LiteraturMITCHELL, A., 2012, The Esri Guide to GIS Analysis - Modeling Suitability, Movement, and Interaction (3. Auflage), ESRI Press, Redlands, California
406-0242-AALAnalysis II Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-7 KP15RM. Akka Ginosar
KurzbeschreibungMathematical tools of an engineer
LernzielMathematics as a tool to solve engineering problems, mathematical formulation of problems in science and engineering. Basic mathematical knowledge of an engineers.
InhaltMulti variable calculus: gradient, directional derivative, chain rule, Taylor expansion, Lagrange multipliers. Multiple integrals: coordinate transformations, path integrals, integrals over surfaces, divergence theorem, applications in physics. Ordinary differential equations.
LiteraturTextbooks in English:
- J. Stewart: Multivariable Calculus, Thomson Brooks/Cole
- V. I. Smirnov: A course of higher mathematics. Vol. II. Advanced calculus
- W. L. Briggs, L. Cochran: Calculus: Early Transcendentals: International Edition, Pearson Education

- M. Akveld, R. Sperb, Analysis II, vdf
- L. Papula: Mathematik für Ingenieure 2, Vieweg Verlag
406-0243-AALAnalysis I and II Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-14 KP30RM. Akka Ginosar
KurzbeschreibungMathematical tools for the engineer
LernzielMathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers.
InhaltComplex numbers.
Calculus for functions of one variable with applications.
Simple Mathematical models in engineering.

Multi variable calculus: gradient, directional derivative, chain rule, Taylor expansion, Lagrange multipliers. Multiple integrals: coordinate transformations, path integrals, integrals over surfaces, divergence theorem, applications in physics. Ordinary differential equations.
LiteraturTextbooks in English:
- J. Stewart: Calculus, Cengage Learning, 2009, ISBN 978-0-538-73365-6.
- J. Stewart: Multivariable Calculus, Thomson Brooks/Cole.
- V. I. Smirnov: A course of higher mathematics. Vol. II. Advanced calculus.
- W. L. Briggs, L. Cochran: Calculus: Early Transcendentals: International Edition, Pearson Education. ISBN 978-0-321-65193-8.
Textbooks in German:
- M. Akveld, R. Sperb: Analysis I, vdf
- M. Akveld, R. Sperb: Analysis II, vdf
- L. Papula: Mathematik für Ingenieure und Naturwissenschaftler, Vieweg Verlag
- L. Papula: Mathematik für Ingenieure 2, Vieweg Verlag
Gesundheitswissenschaften und Technologie Bachelor Information
Wahlfächer
NummerTitelTypECTSUmfangDozierende
551-1003-00LMethoden der Biologischen Analytik Information W3 KP3GR. Aebersold, M. Badertscher, K. Weis
Kurzbeschreibung529-1042-00
Grundlagen der wichtigsten Trennmethoden und der Interpretation von Molekülspektren.

551-1003-00
Der Kurs befasst sich mit den Methoden und ausgewählten Anwendungen von Methoden der Nukleinsäuresequenzierung, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Lernziel529-1042-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten für den Einsatz von relevanten spektroskopischen und Trennmethoden in der analytisch-chemischen Praxis.

551-1003-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten der Methoden für die Bestimmung von Nukleinsäuresequenzen, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Inhalt529-1042-00
Anwendungsorientierte Grundlagen der organischen Instrumentalanalytik und des empirischen Einsatzes von Methoden der Strukturaufklärung (Massenspektrometrie, NMR-, IR-, UV/VIS-Spektroskopie). Grundlagen und Anwendung chromatographischer und elektrophoretischer Trennverfahren. Praxisnahe Anwendung und Vertiefung des Grundwissens anhand von Übungen.

551-1003-00
Der Kurs setzt sich zusammen aus Vorlesungen, die die theoretischen und technischen Grundlagen der betreffenden analytischen Methoden vermitteln und Übungen, die sich mit den Anwendungen der analytischen Methoden in der modernen experimentellen Biologie befassen.
Skript529-1042-00
Ein umfangreiches Skript ist im HCI-Shop erhältlich. Eine Kurzfassung des Teils "Spektroskopie" definiert die für die Prüfung dieses Teils relevanten Themen.
Literatur529-1042-00
- Pretsch E., Bühlmann P., Badertscher M. Structure Determination of Organic Compounds, 5th revised and enlarged English edition, Springer-Verlag, Berlin 2009;
- Pretsch E., Bühlmann P., Badertscher M., Spektroskopische Daten zur Strukturaufklärung organischer Verbindungen, fünfte Auflage, Springer-Verlag, Berlin 2010;
- D.A. Skoog, J.J. Leary, Instrumentelle Analytik, Grundlagen, Geräte, Anwendungen, Springer, Berlin, 1996;
- K. Cammann, Instrumentelle Analytische Chemie, Verfahren, Anwendungen, Qualitätssicherung, Spektrum Akademischer Verlag, Heidelberg, 2001;
- R. Kellner, J.-M. Mermet, M. Otto, H.M. Widmer, Analytical Chemistry, Wiley-VCH Verlag, Weinheim, 1998;
- K. Robards, P.R.Haddad, P.E. Jackson, Principles and practice of modern chromatographic methods, Academic Press, London, 1994;
Voraussetzungen / Besonderes529-1042-00
Voraussetzungen:
- 529-1001-01 V "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1001-00 P "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1011-00 G "Organische Chemie I (für Biol./Pharm.Wiss.)"
Gesundheitswissenschaften und Technologie Master Information
Vertiefung in Bewegungswissenschaften und Sport
Wahlfächer
Wahlfächer II
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
376-2019-00LAngewandte Bewegungsanalyse Information W2 KP2GR. Scharpf, S. Lorenzetti
KurzbeschreibungAnhand von praktischen Beispielen aus Sport, Alltag und Therapie werden verschiedene Methoden der Bewegungsanalyse angewendet und verglichen.
LernzielDie Studierenden können menschliche Bewegungen mithilfe verschiedener Methoden der Bewegungsanalyse gezielt beurteilen.
InhaltIm Verlauf des Studiums lernen Studierende verschiedene Methoden der Bewegungsanalyse kennen: Funktionale, morphologische, klinische, mechanische, systemdynamische, usw.
Diese werden anhand von konkreten Beispielen angewendet und gegenübergestellt. Basis bilden Bewegungen aus Sport, Alltag und Therapie wie Unihockey, Geräteturnen/ Akrobatik, Badminton, Gehen/ Laufen, Krafttraining.
In einer ersten Phase der Vorlesung werden die Ansätze im Plenum vorgestellt und praktisch umgesetzt. In einer zweiten werden individuelle Projekte in kleinen Teams ausgearbeitet, vorgestellt und bewertet.
SkriptAllfällige Unterlagen werden auf moodle zur Verfügung gestellt.
Vertiefung in Medizintechnik
Wahlfächer
Wahlfächer II
NummerTitelTypECTSUmfangDozierende
227-0391-00LMedical Image Analysis
Findet dieses Semester nicht statt.
W3 KP2GE. Konukoglu
KurzbeschreibungIt is the objective of this lecture to introduce the basic concepts used
in Medical Image Analysis. In particular the lecture focuses on shape
representation schemes, segmentation techniques, and the various image registration methods commonly used in Medical Image Analysis applications.
LernzielThis lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
Voraussetzungen / BesonderesBasic knowledge of computer vision would be helpful.
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
227-0969-00LMethods & Models for fMRI Data Analysis Information W6 KP4VK. Stephan
KurzbeschreibungThis course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data.
LernzielTo obtain in-depth knowledge of the theoretical foundations of SPM
and DCM and of their application to empirical fMRI data.
InhaltThis course teaches state-of-the-art methods and models for fMRI data analysis. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of studies in psychiatry, neurology and neuroeconomics.
Vertiefung in Molekulare Gesundheitswissenschaften
Wahlfächer
Wahlfächer II
NummerTitelTypECTSUmfangDozierende
551-1003-00LMethoden der Biologischen Analytik Information W3 KP3GR. Aebersold, M. Badertscher, K. Weis
Kurzbeschreibung529-1042-00
Grundlagen der wichtigsten Trennmethoden und der Interpretation von Molekülspektren.

551-1003-00
Der Kurs befasst sich mit den Methoden und ausgewählten Anwendungen von Methoden der Nukleinsäuresequenzierung, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Lernziel529-1042-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten für den Einsatz von relevanten spektroskopischen und Trennmethoden in der analytisch-chemischen Praxis.

551-1003-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten der Methoden für die Bestimmung von Nukleinsäuresequenzen, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Inhalt529-1042-00
Anwendungsorientierte Grundlagen der organischen Instrumentalanalytik und des empirischen Einsatzes von Methoden der Strukturaufklärung (Massenspektrometrie, NMR-, IR-, UV/VIS-Spektroskopie). Grundlagen und Anwendung chromatographischer und elektrophoretischer Trennverfahren. Praxisnahe Anwendung und Vertiefung des Grundwissens anhand von Übungen.

551-1003-00
Der Kurs setzt sich zusammen aus Vorlesungen, die die theoretischen und technischen Grundlagen der betreffenden analytischen Methoden vermitteln und Übungen, die sich mit den Anwendungen der analytischen Methoden in der modernen experimentellen Biologie befassen.
Skript529-1042-00
Ein umfangreiches Skript ist im HCI-Shop erhältlich. Eine Kurzfassung des Teils "Spektroskopie" definiert die für die Prüfung dieses Teils relevanten Themen.
Literatur529-1042-00
- Pretsch E., Bühlmann P., Badertscher M. Structure Determination of Organic Compounds, 5th revised and enlarged English edition, Springer-Verlag, Berlin 2009;
- Pretsch E., Bühlmann P., Badertscher M., Spektroskopische Daten zur Strukturaufklärung organischer Verbindungen, fünfte Auflage, Springer-Verlag, Berlin 2010;
- D.A. Skoog, J.J. Leary, Instrumentelle Analytik, Grundlagen, Geräte, Anwendungen, Springer, Berlin, 1996;
- K. Cammann, Instrumentelle Analytische Chemie, Verfahren, Anwendungen, Qualitätssicherung, Spektrum Akademischer Verlag, Heidelberg, 2001;
- R. Kellner, J.-M. Mermet, M. Otto, H.M. Widmer, Analytical Chemistry, Wiley-VCH Verlag, Weinheim, 1998;
- K. Robards, P.R.Haddad, P.E. Jackson, Principles and practice of modern chromatographic methods, Academic Press, London, 1994;
Voraussetzungen / Besonderes529-1042-00
Voraussetzungen:
- 529-1001-01 V "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1001-00 P "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1011-00 G "Organische Chemie I (für Biol./Pharm.Wiss.)"
Vertiefung in Neurowissenschaften
Wahlfächer
Wahlfächer II
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Hochenergie-Physik MSc (Joint Master mit EP Paris) Information
Physikalische und mathematische Wahlfächer
Wahlfächer in Mathematik
NummerTitelTypECTSUmfangDozierende
401-3461-00LFunctional Analysis I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Voraussetzungen / BesonderesSolid background on the content of all Mathematics courses of the first two years of the undergraduate curriculum at ETH (most remarkably: fluency with measure theory, Lebesgue integration and L^p spaces).
Informatik Bachelor Information
Bachelor-Studium (Studienreglement 2016)
Grundlagenfächer
NummerTitelTypECTSUmfangDozierende
401-0213-16LAnalysis II Information O5 KP2V + 2UÖ. Imamoglu
KurzbeschreibungDifferential and Integral calculus in many variables, vector analysis.
LernzielDifferential and Integral calculus in many variables, vector analysis.
InhaltDifferential and Integral calculus in many variables, vector analysis.
LiteraturFür allgemeine Informationen, sehen Sie bitte die Webseite der Vorlesung: https://metaphor.ethz.ch/x/2017/hs/401-0213-16L/
Ergänzung
NummerTitelTypECTSUmfangDozierende
651-4271-00LErdwissenschaftliche Datenanalyse und Visualisierung mit MatlabW3 KP3GS. Wiemer, G. De Souza, T. Tormann
KurzbeschreibungDie Vorlesung und dazugehörige Übung geben den Studierenden eine Einführung in die Konzepte und Werkzeuge der wissenschaftlichen Datenanalyse. Anhand von praktischen erdwissenschaftlichen Problemstellungen werden in Kleingruppen und Einzelarbeit Aufgaben von wachsender Komplexität mit der Software MATLAB gelöst. Dabei lernen die Studierenden auch, Datensätze effektvoll zu visualisieren.
LernzielDie folgenden Konzepte werden vorgestellt:
- Effektvolle Datenanalyse und Visualisierung in 2D und 3D
- Arbeiten mit Matrizen und Arrays
- Programmieren und Algorithmenentwicklung
- Animationen sinnvoll einsetzen
- Einen Datensatz statistisch erfassen
- Interaktives Datamining
- Unsicherheiten, Fehlerfortpflanzung und Bootstrapping
- Regressionsanalysen
- Testen von Hypothesen
Integrated Building Systems Master Information
Hauptfächer
Vertiefungsfächer
NummerTitelTypECTSUmfangDozierende
101-0187-00LStructural Reliability and Risk Analysis Information W3 KP2GS. Marelli
KurzbeschreibungStructural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.
LernzielThe goal of this course is to provide the students with a thorough understanding of the key concepts behind structural reliability and risk analysis. After this course the students will have refreshed their knowledge of probability theory and statistics to model uncertainties in view of engineering applications. They will be able to analyze the reliability of a structure and to use risk assessment methods for decision making under uncertain conditions. They will be aware of the state-of-the-art computational methods and software in this field.
InhaltEngineers are confronted every day to decision making under limited amount of information and uncertain conditions. When designing new structures and systems, the design codes such as SIA or Euro- codes usually provide a framework that guarantees safety and reliability. However the level of safety is not quantified explicitly, which does not allow the analyst to properly choose between design variants and evaluate a total cost in case of failure. In contrast, the framework of risk analysis allows one to incorporate the uncertainty in decision making.

The first part of the course is a reminder on probability theory that is used as a main tool for reliability and risk analysis. Classical concepts such as random variables and vectors, dependence and correlation are recalled. Basic statistical inference methods used for building a probabilistic model from the available data, e.g. the maximum likelihood method, are presented.

The second part is related to structural reliability analysis, i.e. methods that allow one to compute probabilities of failure of a given system with respect to prescribed criteria. The framework of reliability analysis is first set up. Reliability indices are introduced together with the first order-second moment method (FOSM) and the first order reliability method (FORM). Methods based on Monte Carlo simulation are then reviewed and illustrated through various examples. By-products of reliability analysis such as sensitivity measures and partial safety coefficients are derived and their links to structural design codes is shown. The reliability of structural systems is also introduced as well as the methods used to reassess existing structures based on new information.

The third part of the course addresses risk assessment methods. Techniques for the identification of hazard scenarios and their representation by fault trees and event trees are described. Risk is defined with respect to the concept of expected utility in the framework of decision making. Elements of Bayesian decision making, i.e. pre-, post and pre-post risk assessment methods are presented.

The course also includes a tutorial using the UQLab software dedicated to real world structural reliability analysis.
SkriptSlides of the lectures are available online every week. A printed version of the full set of slides is proposed to the students at the beginning of the semester.
LiteraturAng, A. and Tang, W.H, Probability Concepts in Engineering - Emphasis on Applications to Civil and Environmental Engineering, 2nd Edition, John Wiley & Sons, 2007.

S. Marelli, R. Schöbi, B. Sudret, UQLab user manual - Structural reliability (rare events estimation), Report UQLab-V0.92-107.
Voraussetzungen / BesonderesBasic course on probability theory and statistics
Interdisziplinäre Naturwissenschaften Bachelor Information
Physikalisch-Chemischen Fachrichtung
1. Semester (Physikalisch-Chemische Richtung)
Obligatorische Fächer Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-1261-07LAnalysis I Information O10 KP6V + 3UM. Einsiedler
KurzbeschreibungEinführung in die Differential- und Integralrechnung in einer reellen Veränderlichen: Grundbegriffe des mathematischen Denkens, Zahlen, Folgen und Reihen, topologische Grundbegriffe, stetige Funktionen, differenzierbare Funktionen, gewöhnliche Differentialgleichungen, Riemannsche Integration.
LernzielMathematisch exakter Umgang mit Grundbegriffen der Differential-und Integralrechnung.
LiteraturH. Amann, J. Escher: Analysis I
https://link.springer.com/book/10.1007/978-3-7643-7756-4

J. Appell: Analysis in Beispielen und Gegenbeispielen
https://link.springer.com/book/10.1007/978-3-540-88903-8

R. Courant: Vorlesungen über Differential- und Integralrechnung
https://link.springer.com/book/10.1007/978-3-642-61988-5

O. Forster: Analysis 1
https://link.springer.com/book/10.1007/978-3-658-00317-3

H. Heuser: Lehrbuch der Analysis
https://link.springer.com/book/10.1007/978-3-322-96828-9

K. Königsberger: Analysis 1
https://link.springer.com/book/10.1007/978-3-642-18490-1

W. Walter: Analysis 1
https://link.springer.com/book/10.1007/3-540-35078-0

V. Zorich: Mathematical Analysis I (englisch)
https://link.springer.com/book/10.1007/978-3-662-48792-1

A. Beutelspacher: "Das ist o.B.d.A. trivial"
https://link.springer.com/book/10.1007/978-3-8348-9599-8

H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten
https://link.springer.com/book/10.1007/978-3-642-28646-9
3. Semester (Physikalisch-Chemische Richtung)
Wahlfächer
Im Bachelor-Studiengang Interdisziplinäre Naturwissenschaften können die Studierenden prinzipiell alle Lehrveranstaltungen wählen, die in einem Bachelor-Studiengang der ETH angeboten werden.

Zu Beginn des 2. Studienjahrs legt jeder Studierende in Absprache mit dem Studiendelegierten für Interdisziplinäre Naturwissenschaften sein/ihr individuelles Studienprogramm fest. Siehe Studienreglement 2010 für Details.
NummerTitelTypECTSUmfangDozierende
401-2303-00LFunktionentheorie Information W6 KP3V + 2UR. Pandharipande
KurzbeschreibungKomplexe Funktionen einer komplexen Veränderlichen, Cauchy-Riemann Gleichungen, Cauchyscher Integralsatz, Singularitäten, Residuensatz, Umlaufzahl, analytische Fortsetzung, spezielle Funktionen, konforme Abbildungen. Riemannscher Abbildungssatz.
LernzielFähigkeit zum Umgang mit analytischen Funktion; insbesondre Anwendungen des Residuensatzes
LiteraturTh. Gamelin: Complex Analysis. Springer 2001

E. Titchmarsh: The Theory of Functions. Oxford University Press

D. Salamon: "Funktionentheorie". Birkhauser, 2011. (In German)

L. Ahlfors: "Complex analysis. An introduction to the theory of analytic functions of one complex variable." International Series in Pure and Applied Mathematics. McGraw-Hill Book Co.

B. Palka: "An introduction to complex function theory."
Undergraduate Texts in Mathematics. Springer-Verlag, 1991.

K.Jaenich: Funktionentheorie. Springer Verlag

R.Remmert: Funktionentheorie I. Springer Verlag

E.Hille: Analytic Function Theory. AMS Chelsea Publications
Biochemisch-Physikalischen Fachrichtung
1. Semester (Biochemisch-Physikalische Richtung)
Obligatorische Fächer Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0271-00LGrundlagen der Mathematik I (Analysis A)W5 KP3V + 2UL. Keller
KurzbeschreibungAnwendungsorientierte Einführung in die eindimensionale Analysis. Einfache Modelle kennen, selber bilden und mathematisch analysieren können.
Funktionen einer Variablen: Funktionsbegriff, Ableitungsbegriff, die Idee der Differentialgleichung, komplexe Zahlen, Taylorpolynome und Taylorreihen. Integrale von Funktionen einer Variablen.
LernzielGrundlegende Begriffe der eindimensionalen Analysis kennen und mit ihnen umgehen können. Einfache Modelle kennen oder selber bilden und mathematisch analysieren.
InhaltFunktionen einer Variablen: Funktionsbegriff, Ableitungsbegriff, die Idee der Differentialgleichung, komplexe Zahlen, Taylorpolynome und Taylorreihen. Integrale von Funktionen einer Variablen.
LiteraturG. B. Thomas, M. D. Weir, J. Hass: Analysis 1, Lehr- und Übungsbuch, Pearson-Verlag
D. W. Jordan, P. Smith: Mathematische Methoden für die Praxis, Spektrum Akademischer Verlag
R. Sperb/M. Akveld: Analysis I (vdf)
L. Papula: Mathematik für Ingenieure und Naturwissenschaftler (3 Bände), Vieweg
weitere Literatur wird in der Vorlesung angegeben
401-1261-07LAnalysis I Information W10 KP6V + 3UM. Einsiedler
KurzbeschreibungEinführung in die Differential- und Integralrechnung in einer reellen Veränderlichen: Grundbegriffe des mathematischen Denkens, Zahlen, Folgen und Reihen, topologische Grundbegriffe, stetige Funktionen, differenzierbare Funktionen, gewöhnliche Differentialgleichungen, Riemannsche Integration.
LernzielMathematisch exakter Umgang mit Grundbegriffen der Differential-und Integralrechnung.
LiteraturH. Amann, J. Escher: Analysis I
https://link.springer.com/book/10.1007/978-3-7643-7756-4

J. Appell: Analysis in Beispielen und Gegenbeispielen
https://link.springer.com/book/10.1007/978-3-540-88903-8

R. Courant: Vorlesungen über Differential- und Integralrechnung
https://link.springer.com/book/10.1007/978-3-642-61988-5

O. Forster: Analysis 1
https://link.springer.com/book/10.1007/978-3-658-00317-3

H. Heuser: Lehrbuch der Analysis
https://link.springer.com/book/10.1007/978-3-322-96828-9

K. Königsberger: Analysis 1
https://link.springer.com/book/10.1007/978-3-642-18490-1

W. Walter: Analysis 1
https://link.springer.com/book/10.1007/3-540-35078-0

V. Zorich: Mathematical Analysis I (englisch)
https://link.springer.com/book/10.1007/978-3-662-48792-1

A. Beutelspacher: "Das ist o.B.d.A. trivial"
https://link.springer.com/book/10.1007/978-3-8348-9599-8

H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten
https://link.springer.com/book/10.1007/978-3-642-28646-9
401-0231-10LAnalysis IW8 KP4V + 3UT. H. Willwacher
KurzbeschreibungReelle und komplexe Zahlen, Vektoren, Grenzwerte, Folgen, Reihen, Potenzreihen, stetige Abbildungen, Differential- und Integralrechnung einer Variablen, Einführung in gewöhnliche Differentialgleichungen
LernzielEinfuehrung in die Grundlagen der Analysis
SkriptKonrad Koenigsberger, Analysis I.
Christian Blatter: Ingenieur-Analysis (Kapitel 1-3)
3. Semester (Biochemisch-Physikalische Richtung)
Obligatorische Fächer: Prüfungsblock
NummerTitelTypECTSUmfangDozierende
401-0353-00LAnalysis III Information W4 KP2V + 1UA. Figalli
KurzbeschreibungIn dieser Lehrveranstaltung werden Probleme der angewandten Analysis behandelt, speziell ausgerichtet auf die Bedürfnisse der Elektrotechniker. Dazu gehört vor allem das Studium der einfachsten Fälle der drei Grundtypen von partiellen Differentialgleichungen zweiten Grades: Laplace-Gleichung, Wärmeleitungsgleichung und Wellengleichung.
Lernziel
Inhalt1.) Klassifizierung von PDE's
- linear, quasilinear, nicht-linear
- elliptisch, parabolisch, hyperbolisch

2.) Quasilineare PDE
- Methode der Charakteristiken (Beispiele)

3.) Elliptische PDE
- Bsp: Laplace-Gleichung
- Harmonische Funktionen, Maximumsprinzip, Mittelwerts-Formel.
- Methode der Variablenseparation.

4.) Parabolische PDE
- Bsp: Wärmeleitungsgleichung
- Bsp: Inverse Wärmeleitungsgleichung
- Methode der Variablenseparation

5.) Hyperbolische PDE
- Bsp: Wellengleichung
- Formel von d'Alembert in (1+1)-Dimensionen
- Methode der Variablenseparation

6.) Green'sche Funktionen
- Rechnen mit der Dirac-Deltafunktion
- Idee der Green'schen Funktionen (Beispiele)

7.) Ausblick auf numerische Methoden
- 5-Punkt-Diskretisierung des Laplace-Operators (Beispiele)
LiteraturY. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005)

Zusätzliche Literatur:
Erwin Kreyszig, "Advanced Engineering Mathematics", John Wiley & Sons, Kap. 8, 11, 16 (sehr gutes Buch, als Referenz zu benutzen)
Norbert Hungerbühler, "Einführung in die partiellen Differentialgleichungen", vdf Hochschulverlag AG an der ETH Zürich.
G. Felder:Partielle Differenzialgleichungen.
https://people.math.ethz.ch/~felder/PDG/
Voraussetzungen / BesonderesVoraussetzungen: Analysis I und II, Fourier Reihen (Komplexe Analysis)
Wahlfächer
Im Bachelor-Studiengang Interdisziplinäre Naturwissenschaften können die Studierenden prinzipiell alle Lehrveranstaltungen wählen, die in einem Bachelor-Studiengang der ETH angeboten werden.

Zu Beginn des 2. Studienjahrs legt jeder Studierende in Absprache mit dem Studiendelegierten für Interdisziplinäre Naturwissenschaften sein/ihr individuelles Studienprogramm fest. Siehe Studienreglement 2010 für Details.
NummerTitelTypECTSUmfangDozierende
401-2303-00LFunktionentheorie Information W6 KP3V + 2UR. Pandharipande
KurzbeschreibungKomplexe Funktionen einer komplexen Veränderlichen, Cauchy-Riemann Gleichungen, Cauchyscher Integralsatz, Singularitäten, Residuensatz, Umlaufzahl, analytische Fortsetzung, spezielle Funktionen, konforme Abbildungen. Riemannscher Abbildungssatz.
LernzielFähigkeit zum Umgang mit analytischen Funktion; insbesondre Anwendungen des Residuensatzes
LiteraturTh. Gamelin: Complex Analysis. Springer 2001

E. Titchmarsh: The Theory of Functions. Oxford University Press

D. Salamon: "Funktionentheorie". Birkhauser, 2011. (In German)

L. Ahlfors: "Complex analysis. An introduction to the theory of analytic functions of one complex variable." International Series in Pure and Applied Mathematics. McGraw-Hill Book Co.

B. Palka: "An introduction to complex function theory."
Undergraduate Texts in Mathematics. Springer-Verlag, 1991.

K.Jaenich: Funktionentheorie. Springer Verlag

R.Remmert: Funktionentheorie I. Springer Verlag

E.Hille: Analytic Function Theory. AMS Chelsea Publications
Lebensmittelwissenschaften Bachelor Information
1. Semester
Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0251-00LMathematik I: Analysis I und Lineare AlgebraO6 KP4V + 2UL. Halbeisen
KurzbeschreibungDiese Vorlesung behandelt mathematische Konzepte und Methoden, die zum Modellieren, Lösen und Diskutieren wissenschaftlicher Probleme nötig sind - speziell durch gewöhnliche Differentialgleichungen.
LernzielMathematik ist von immer grösserer Bedeutung in den Natur- und Ingenieurwissenschaften. Grund dafür ist das folgende Konzept zur Lösung konkreter Probleme: Der entsprechende Ausschnitt der Wirklichkeit wird in der Sprache der Mathematik modelliert; im mathematischen Modell wird das Problem - oft unter Anwendung von äusserst effizienter Software - gelöst und das Resultat in die Realität zurück übersetzt.

Ziel der Vorlesungen Mathematik I und II ist es, die einschlägigen mathematischen Grundlagen bereit zu stellen. Differentialgleichungen sind das weitaus wichtigste Hilfsmittel im Prozess des Modellierens und stehen deshalb im Zentrum beider Vorlesungen.
Inhalt1. Differential- und Integralrechnung:
Wiederholung der Ableitung, Linearisierung, Taylor-Polynome, Extremwerte, Stammfunktion, Hauptsatz der Differential- und Integralrechnung, Integrationsmethoden, uneigentliche Integrale.

2. Lineare Algebra und Komplexe Zahlen:
lineare Gleichungssysteme, Gauss-Verfahren, Matrizen, Determinanten, Eigenwerte und Eigenvektoren, Darstellungsformen der komplexe Zahlen, Potenzieren, Radizieren, Fundamentalsatz der Algebra.

3. Gewöhnliche Differentialgleichungen:
Separierbare Differentialgleichungen (DGL), Integration durch Substitution, Lineare DGL erster und zweiter Ordnung, homogene Systeme linearer DGL mit konstanten Koeffizienten, Einführung in die dynamischen Systeme in der Ebene.
Literatur- Thomas, G. B., Weir, M. D. und Hass, J.: Analysis 1, Lehr- und Übungsbuch (Pearson).
- Gramlich, G.: Lineare Algebra, eine Einführung (Hanser).
- Papula, L.: Mathematik für Ingenieure und Naturwissenschaftler, Bd. 1 und 2 (Vieweg+Teubner).
Voraussetzungen / BesonderesVoraussetzungen: Vertrautheit mit den Grundlagen der Analysis, insbesondere mit dem Funktions- und Ableitungsbegriff.

Mathe-Lab (Präsenzstunden):
Mo 12-14, Di 17-19, Mi 17-19, stets im Raum HG E 41.
3. Semester
Grundlagenfächer II
Prüfungsblock 1
NummerTitelTypECTSUmfangDozierende
701-0071-00LMathematik III: SystemanalyseO4 KP2V + 1UN. Gruber, M. Vogt
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
5. Semester
Lebensmittelwissenschaftliche Fächer
NummerTitelTypECTSUmfangDozierende
752-1103-00LLebensmittelanalytik IIW+3 KP2VT. Gude
KurzbeschreibungKennenlernen der Grundlagen und Anwendungen der Massenspektrometrie in der Lebensmittelanalytik.
LernzielKennenlernen der Grundlagen und Anwendungen der Massenspektrometrie in der Lebensmittelanalytik.
InhaltSchwerpunkt: Massenspektrometrie, Anwendungen der Massenspektrometrie (MS).
SkriptEs werden Beilagen zur Vorlesung abgegeben.
Wahlfächer (NUR für Studienreglement 2016)
Eine Wahlfachliste wird separat publiziert.
NummerTitelTypECTSUmfangDozierende
551-1003-00LMethoden der Biologischen Analytik Information W3 KP3GR. Aebersold, M. Badertscher, K. Weis
Kurzbeschreibung529-1042-00
Grundlagen der wichtigsten Trennmethoden und der Interpretation von Molekülspektren.

551-1003-00
Der Kurs befasst sich mit den Methoden und ausgewählten Anwendungen von Methoden der Nukleinsäuresequenzierung, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Lernziel529-1042-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten für den Einsatz von relevanten spektroskopischen und Trennmethoden in der analytisch-chemischen Praxis.

551-1003-00
Kenntnis der notwendigen Grundlagen und der Anwendungsmöglichkeiten der Methoden für die Bestimmung von Nukleinsäuresequenzen, der massenspektrometrischen Analyse von Proteinen und Proteomen und Licht-und Fluoreszenz gestützten Methoden der Mikroskopie.
Inhalt529-1042-00
Anwendungsorientierte Grundlagen der organischen Instrumentalanalytik und des empirischen Einsatzes von Methoden der Strukturaufklärung (Massenspektrometrie, NMR-, IR-, UV/VIS-Spektroskopie). Grundlagen und Anwendung chromatographischer und elektrophoretischer Trennverfahren. Praxisnahe Anwendung und Vertiefung des Grundwissens anhand von Übungen.

551-1003-00
Der Kurs setzt sich zusammen aus Vorlesungen, die die theoretischen und technischen Grundlagen der betreffenden analytischen Methoden vermitteln und Übungen, die sich mit den Anwendungen der analytischen Methoden in der modernen experimentellen Biologie befassen.
Skript529-1042-00
Ein umfangreiches Skript ist im HCI-Shop erhältlich. Eine Kurzfassung des Teils "Spektroskopie" definiert die für die Prüfung dieses Teils relevanten Themen.
Literatur529-1042-00
- Pretsch E., Bühlmann P., Badertscher M. Structure Determination of Organic Compounds, 5th revised and enlarged English edition, Springer-Verlag, Berlin 2009;
- Pretsch E., Bühlmann P., Badertscher M., Spektroskopische Daten zur Strukturaufklärung organischer Verbindungen, fünfte Auflage, Springer-Verlag, Berlin 2010;
- D.A. Skoog, J.J. Leary, Instrumentelle Analytik, Grundlagen, Geräte, Anwendungen, Springer, Berlin, 1996;
- K. Cammann, Instrumentelle Analytische Chemie, Verfahren, Anwendungen, Qualitätssicherung, Spektrum Akademischer Verlag, Heidelberg, 2001;
- R. Kellner, J.-M. Mermet, M. Otto, H.M. Widmer, Analytical Chemistry, Wiley-VCH Verlag, Weinheim, 1998;
- K. Robards, P.R.Haddad, P.E. Jackson, Principles and practice of modern chromatographic methods, Academic Press, London, 1994;
Voraussetzungen / Besonderes529-1042-00
Voraussetzungen:
- 529-1001-01 V "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1001-00 P "Allgemeine Chemie I (für Biol./Pharm.Wiss.)"
- 529-1011-00 G "Organische Chemie I (für Biol./Pharm.Wiss.)"
Lebensmittelwissenschaft Master Information
Vertiefung in Food Processing
Methodische Fächer
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W+5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Vertiefung in Food Quality and Safety
Methodische Fächer
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W+5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Vertiefung in Nutrition and Health
Methodische Fächer
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W+5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Optionale Fächer
NummerTitelTypECTSUmfangDozierende
766-6205-00LNutrient Analysis in Foods Belegung eingeschränkt - Details anzeigen
Number of participants limited to 15.
Permission from lecturers required for all students.
W3 KP3UM. B. Zimmermann, H. C. Winkler
KurzbeschreibungIn this practical course different meals are prepared and then analysed in the laboratory. The analyses comprise energy, macronutrients, specific micronutrients as well as polyphenols and phytic acid. Based on these results, the nutritional value of each meal is critically evaluated and discussed.
LernzielLearning analytical methods to determine macro- and micronutrient content in foods. Critical evaluation of analytical results, critical comparison with values from food composition tables, and interpretation in relation to nutritional value of meals.
InhaltThe practical course nutrient analysis in foods includes the meal preparation (2 hours in December 2017, date to be defined) and chemical analysis of five meals from 5 different types of diets (students will work in groups; one meal per group). The content of macronutrients, specific micronutrients and secondary plant components are analysed using common analytical methods. The analytical results are compared with calculated data from food composition databases by using the nutrition software EbisPro and critically evaluated. The nutritional values of the meals in relation to specific chronic diseases and iron bioavailability are discussed. The practical course is accompanied by a lecture on the basic principles of analytical chemistry.
SkriptA script and lecture slides are handed out before course start.
Voraussetzungen / BesonderesStudents will work in groups.
Performance is assessed by a short test on course content, oral presentation of results and a short report.
Attendance is compulsory for the lecture, the laboratory work and the oral presentation.
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
752-1101-AALFood Analysis I
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-3 KP6RL. Nyström
KurzbeschreibungTo understand the basic principles of analytical chemistry. To get acquainted with the principles and applications of important routine methods of instrumental food analysis (UV/VIS, IR, AAS, GC, HPLC).
LernzielTo understand the basic principles of analytical chemistry. To get acquainted with the principles and applications of important routine methods of instrumental food analysis (UV/VIS, IR, AAS, GC, HPLC).
InhaltFundamentals: Chemical concentrations. The analytical process (sampling, sample preparation, calibration, measurement, statistical evaluation of analytical results). Errors in quantitative analysis. Important parameters of an analytical procedure (accuracy, precision, limit of detection, sensitivity, specificity/selectivity).

Methods: Optical spectroscopy (basic principles, UV/VIS, IR, and atomic absorption spectroscopy). Chromatography (GC, HPLC).
SkriptThe lectures are supplemented with handouts.
LiteraturFood Analysis - Fourth Edition, edited by S. Suzanne Nielson; 2010; Springer, Selected sections.
701-0071-AALMathematics III: Systems Analysis
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RN. Gruber
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
MAS in Entwicklung und Zusammenarbeit Information
Die Vorlesungen und Weiterbildungskurse des NADEL sind ausschliesslich für Studierende des MAS in Entwicklung und Zusammenarbeit und für Fachkräfte der Entwicklungszusammenarbeit (EZA) mit mindestens 2 Jahren Berufserfahrung in der EZA und einem von der ETH anerkannten Abschluss auf Masterstufe zugänglich. Doktorierende, die sich mit empirischer Forschung im EZA-Bereich befassen, können "sur Dossier" zugelassen werden.
Vertiefungsmodule
NummerTitelTypECTSUmfangDozierende
865-0000-06LWirkungsanalysen: Methoden und Anwendungen
Nur für Studierende des MAS bzw. CAS in Entwicklung und Zusammenarbeit sowie Fachkräfte mit mind. 24 Monaten Berufserfahrung in der internationalen Zusammenarbeit.
Doktoranden, die sich mit empirischer Forschung im EZA-Bereich befassen, können "sur Dossier" zugelassen werden.

Einschreibung nur über das NADEL-Sekretariat.
W2 KP3GI. Günther
KurzbeschreibungDie Veranstaltung bietet einen Überblick über verschiedene Methoden, die für eine aussagekräftige und fundierte Analyse der Auswirkungen von Entwicklungsprogrammen und -projekten herangezogen werden können. Die Veranstaltung vermittelt sowohl grundlegende Methodenkenntnisse als auch Praxisbeispiele aus der Entwicklungszusammenarbeit von bi- und multilateralen Gebern und NGOs.
LernzielDie Teilnehmer kennen die wichtigsten Methoden für rigorose Wirkungsanalysen und sind in der Lage, für bestehende Programme und Projekte der Entwicklungszusammenarbeit kleinere Wirkungsanalysen selbst durchzuführen und umfangreiche Wirkungsanalysen in Auftrag zu geben und zu verfolgen. Des Weiteren können Teilnehmer die Ergebnisse eigener und externer Wirkungsanalysen effektiv nutzen.
InhaltEinführung in rigorose Wirkungsanalysen; Anwendungsbereiche und Beispiele; Vermittlung grundlegender statistischer Kenntnisse für Wirkungsanalysen; Vor- und Nachteile quantitativer Analysen; Experimentelle und quasi-experimentelle Methoden; Auswahl geeigneter Indikatoren; Vollstaendige Wirkungsketten; Datenerhebung, -management und -analyse; Projektmanagement einer Wirkungsanalyse.
Voraussetzungen / BesonderesDer Besuch der Lehrveranstaltung ist an Voraussetzungen gebunden, die der Homepage des NADEL zu entnehmen sind. Elektronische Einschreibung darf erst nach Einschreibung am NADEL-Sekretariat erfolgen.
MAS in Ernährung und Gesundheit Information
Disziplinäre Fächer
NummerTitelTypECTSUmfangDozierende
766-6205-00LNutrient Analysis in Foods Belegung eingeschränkt - Details anzeigen
Number of participants limited to 15.
Permission from lecturers required for all students.
W+3 KP3UM. B. Zimmermann, H. C. Winkler
KurzbeschreibungIn this practical course different meals are prepared and then analysed in the laboratory. The analyses comprise energy, macronutrients, specific micronutrients as well as polyphenols and phytic acid. Based on these results, the nutritional value of each meal is critically evaluated and discussed.
LernzielLearning analytical methods to determine macro- and micronutrient content in foods. Critical evaluation of analytical results, critical comparison with values from food composition tables, and interpretation in relation to nutritional value of meals.
InhaltThe practical course nutrient analysis in foods includes the meal preparation (2 hours in December 2017, date to be defined) and chemical analysis of five meals from 5 different types of diets (students will work in groups; one meal per group). The content of macronutrients, specific micronutrients and secondary plant components are analysed using common analytical methods. The analytical results are compared with calculated data from food composition databases by using the nutrition software EbisPro and critically evaluated. The nutritional values of the meals in relation to specific chronic diseases and iron bioavailability are discussed. The practical course is accompanied by a lecture on the basic principles of analytical chemistry.
SkriptA script and lecture slides are handed out before course start.
Voraussetzungen / BesonderesStudents will work in groups.
Performance is assessed by a short test on course content, oral presentation of results and a short report.
Attendance is compulsory for the lecture, the laboratory work and the oral presentation.
MAS in Medizinphysik Information
Fachrichtung: Allg. Medizinphysik und Biomedizinisches Ingenieurwesen
Vertiefung Bioimaging
Kernfächer
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
227-0391-00LMedical Image Analysis
Findet dieses Semester nicht statt.
W3 KP2GE. Konukoglu
KurzbeschreibungIt is the objective of this lecture to introduce the basic concepts used
in Medical Image Analysis. In particular the lecture focuses on shape
representation schemes, segmentation techniques, and the various image registration methods commonly used in Medical Image Analysis applications.
LernzielThis lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
Voraussetzungen / BesonderesBasic knowledge of computer vision would be helpful.
227-0969-00LMethods & Models for fMRI Data Analysis Information W6 KP4VK. Stephan
KurzbeschreibungThis course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data.
LernzielTo obtain in-depth knowledge of the theoretical foundations of SPM
and DCM and of their application to empirical fMRI data.
InhaltThis course teaches state-of-the-art methods and models for fMRI data analysis. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of studies in psychiatry, neurology and neuroeconomics.
MAS in Science, Technology and Policy Information
Kernfächer
NummerTitelTypECTSUmfangDozierende
860-0002-00LQuantitative Policy Analysis and ModelingO6 KP4GA. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet
KurzbeschreibungThe lectures will introduce students to the principles of quantitative policy analysis, namely the methods to predict and evaluate the social, economic, and environmental effects of alternative strategies to achieve public objectives. A series of graded assignments will give students an opportunity for students to apply those methods to a set of case studies
LernzielThe objectives of this course are to develop the following key skills necessary for policy analysts:
- Identifying the critical quantitative factors that are of importance to policy makers in a range of decision-making situations.
- Developing conceptual models of the types of processes and relationships governing these quantitative factors, including stock-flow dynamics, feedback loops, optimization, sources and effects of uncertainty, and agent coordination problems.
- Develop and program numerical models to simulate the processes and relationships, in order to identify policy problems and the effects of policy interventions.
- Communicate the findings from these simulations and associated analysis in a manner that makes transparent their theoretical foundation, the level and sources of uncertainty, and ultimately their applicability to the policy problem.
The course will proceed through a series of policy analysis and modeling exercises, involving real-world or hypothetical problems. The specific examples around which work will be done will concern the environment, energy, health, and natural hazards management.
MAS in Sustainable Water Resources Information
Das Masterprogramm (Master of Advanced Studies) in erneuerbaren Wasserressourcen ist ein vollzeitlicher Weiterbildungsdiplomlehrgang über 12 Monate. Der Fokus des Programms liegt auf der Nachhaltigkeit und Wasserressourcen in Lateinamerika, mit einem speziellen Augenmerk auf die Einflüsse von Entwicklung und Klimaveränderung auf die Wasserressourcen. Der Kurs verbindet multidisziplinäre Kursarbeit mit hochrangiger Forschung. Eine Auswahl der Forschungsthemen sind: Wasserqualität, Wasserquantität, Wasser für die Landwirtschaft, Wasser für die Umwelt, Anpassungen an die Klimaveränderung und integrierte Wasserwirtschaft. Sprache: Englisch. Kreditpunkte: 66 ECTS. Für weitere Informationen: http://www.ifu.ethz.ch/MAS_SWR
Kernfächer
Foundation courses: 12 credits have to be achieved.
NummerTitelTypECTSUmfangDozierende
102-0227-00LSystems Analysis and Mathematical Modeling in Urban Water Management Information W6 KP4GE. Morgenroth, M. Maurer
KurzbeschreibungSystematic introduction of material balances, transport processes, kinetics, stoichiometry and conservation. Ideal reactors, residence time distribution, heterogeneous systems, dynamic response of reactors. Parameter identification, local sensitivity, error propagation, Monte Carlo simulation. Introduction to real time control (PID controllers). Extensive coding of examples in Berkeley Madonna.
LernzielThe goal of this course is to provide the students with an understanding and the tools to develop their own mathematical models, to plan experiments, to evaluate error propagation and to test simple process control strategies in the field of process engineering in urban water management.
InhaltThe course will provide a broad introduction into the fundamentals of modeling water treatment systems. The topics are:
- Introduction into modeling and simulation
- The material balance equations, transport processes, transformation processes (kinetics, stoichiometry, conservation)
- Ideal reactors
- Hydraulic residence time distribution and modeling of real reactors
- Dynamic behavior of reactor systems
- Systems analytical tools: Sensitivity, parameter identification, error propagation, Monte Carlo simulation
- Introduction to process control (PID controller, fuzzy control)
SkriptCopies of overheads will be made available.
LiteraturThere will be a required textbook that students need to purchase:
Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg
Voraussetzungen / BesonderesThis course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously.
Wahlfächer
Electives: 6 credits has to be achieved.
NummerTitelTypECTSUmfangDozierende
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
Maschineningenieurwissenschaften Bachelor Information
1. Semester
Die Anmeldung für die Übungsstunden erfolgt über die Applikation https://echo.ethz.ch/ mit Ihrem nETHz Login (Benutzername, Passwort).
Obligatorische Fächer: Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0261-G0LAnalysis I Information O8 KP5V + 3UA. Steiger
KurzbeschreibungDifferential- und Integralrechnung von Funktionen einer und mehrerer Variablen; Vektoranalysis; gewöhnliche Differentialgleichungen erster und höherer Ordnung, Differentialgleichungssysteme; Potenzreihen. In jedem Teilbereich eine grosse Anzahl von Anwendungsbeispielen aus Mechanik, Physik und anderen Lehrgebieten des Ingenieurstudiums.
LernzielEinführung in die mathematischen Grundlagen der Ingenieurwissenschaften, soweit sie die Differential- und Integralrechnung betreffen.
LiteraturU. Stammbach: Analysis I/II, Teil A, B, C und Aufgabensammlung

Die Vorlesung folgt dem Skript von Prof. U. Stammbach. Die vier Bände sind im Gesamtpaket zum Spezialpreis von CHF 75.- nur im ETH Store erhältlich und sehr zu empfehlen. Es findet kein Hörsaalverkauf statt.
Voraussetzungen / BesonderesDie Übungsaufgaben (inkl. Multiple Choice) sind ein wichtiger Bestandteil der Lehrveranstaltung. Es wird erwartet, dass Sie mindestens 75% der wöchentlichen Serien bearbeiten und zur Korrektur einreichen.
3. Semester
Obligatorische Fächer
Prüfungsblock 1
NummerTitelTypECTSUmfangDozierende
401-0363-10LAnalysis IIIO3 KP2V + 1UF. Da Lio
KurzbeschreibungIntroduction to partial differential equations. Differential equations which are important in applications are classified and solved. Elliptic, parabolic and hyperbolic differential equations are treated. The following mathematical tools are introduced: Laplace transforms, Fourier series, separation of variables, methods of characteristics.
LernzielMathematical treatment of problems in science and engineering. To understand the properties of the different types of partial differential equations.

The first lecture is on Thursday, September 28 13-15 in HG F 7 and video transmitted into HG F 5.

The coordinator is Simon Brun
https://www.math.ethz.ch/the-department/people.html?u=brunsi
InhaltLaplace Transforms:
- Laplace Transform, Inverse Laplace Transform, Linearity, s-Shifting
- Transforms of Derivatives and Integrals, ODEs
- Unit Step Function, t-Shifting
- Short Impulses, Dirac's Delta Function, Partial Fractions
- Convolution, Integral Equations
- Differentiation and Integration of Transforms

Fourier Series, Integrals and Transforms:
- Fourier Series
- Functions of Any Period p=2L
- Even and Odd Functions, Half-Range Expansions
- Forced Oscillations
- Approximation by Trigonometric Polynomials
- Fourier Integral
- Fourier Cosine and Sine Transform

Partial Differential Equations:
- Basic Concepts
- Modeling: Vibrating String, Wave Equation
- Solution by separation of variables; use of Fourier series
- D'Alembert Solution of Wave Equation, Characteristics
- Heat Equation: Solution by Fourier Series
- Heat Equation: Solutions by Fourier Integrals and Transforms
- Modeling Membrane: Two Dimensional Wave Equation
- Laplacian in Polar Coordinates: Circular Membrane, Fourier-Bessel Series
- Solution of PDEs by Laplace Transform
SkriptLecture notes by Prof. Dr. Alessandra Iozzi:
https://polybox.ethz.ch/index.php/s/D3K0TayQXvfpCAA
LiteraturE. Kreyszig, Advanced Engineering Mathematics, John Wiley & Sons, 10. Auflage, 2011

C. R. Wylie & L. Barrett, Advanced Engineering Mathematics, McGraw-Hill, 6th ed.

S.J. Farlow, Partial Differential Equations for Scientists and Engineers, Dover Books on Mathematics, NY.

G. Felder, Partielle Differenzialgleichungen für Ingenieurinnen und Ingenieure, hypertextuelle Notizen zur Vorlesung Analysis III im WS 2002/2003.

Y. Pinchover, J. Rubinstein, An Introduction to Partial Differential Equations, Cambridge University Press, 2005

For reference/complement of the Analysis I/II courses:

Christian Blatter: Ingenieur-Analysis
https://people.math.ethz.ch/~blatter/dlp.html
5. Semester
Fokus-Vertiefung
Design, Mechanics and Materials
Fokus-Koordinatorin: Prof. Kristina Shea
Für die erforderlichen 20 KPs der Fokus-Vertiefung Design, Mechanics and Materials sind alle aufgeführten Fächer frei wählbar. Empfohlene Fächer sind gekennzeichnet. Falls Sie einen Kurs auf Masterlevel besuchen möchten, müssen Sie dafür das Einverständnis des zuständigen Dozenten einholen.
NummerTitelTypECTSUmfangDozierende
151-0360-00LMethoden der StrukturanalyseW+4 KP2V + 1UG. Kress
KurzbeschreibungDie Grundlagen der Strukturauslegung werden nach den Kriterien der Festigkeit, der Stabilität, der Ermüdungsauslegung und der elasto-plastischen Strukturanalyse behandelt.
Strukturtheorien (für eindimensionalen und zweidimensionalen Tragwerke) werden auf der Basis der Energie sätze präsentiert.
LernzielErweiterung der Grundlagen zur Behandlung strukturmechanischer Auslegungsproblemen. Einführung in die Dimensionierung von Flächentragwerke. Verständnis des Zusammenhangs zwischen Materialverhalten, Strukturtheorien und Auslegungskriterien.
Inhalt1. Grundproblem der Kontinuumsmechanik und Energiesätze: Herleitung von Strukturtheorien; Homogenisierungstheorien; Finite Elementen; Bruchmechanik.
2. Strukturtheorien für Flächentragwerke und Stabilität: Scheiben, Platten; Beulen von Platten (nichtlineare Plattentheorie)
3. Festigkeitshypothesen und Materialverhalten: Duktiles Verhalten, Plastizität, vMises, Tresca, Hauptspannungshypothese; Sprödes Verhalten; Viskoplastisches Verhalten, Kriechfestigkeit
4. Strukturauslegung: Ermüdung und dynamische Strukturanalyse
SkriptSkript und alle anderen Vorlesungsunterlagen erhältlich auf MOODLE
Voraussetzungen / Besondereskeine
Ingenieur-Tools IV
Die Teilnahme an den Ingenieur-Tools-Kursen ist obligatorisch. Bei Abwesenheit werden keine Kreditpunkte gutgeschrieben. Ausnahmen müssen vom Dozenten bewilligt werden.
NummerTitelTypECTSUmfangDozierende
151-0015-10LIngenieur-Tool IV: Experimentelle Modalanalyse Belegung eingeschränkt - Details anzeigen
Die Ingenieur-Tool-Kurse sind ausschliesslich für MAVT-Bachelor-Studierende.

Maximale Teilnehmerzahl: 16

Es darf nur ein Ingenieur-Tool-Kurs pro Semester belegt werden.
W0.4 KP1KF. Kuster
KurzbeschreibungMess- und Analysemethoden zur Ermittlung von Übertragungsfunktionen mechanischen Strukturen. Auswertung und Aufbereitung der Messdaten zum Visualisieren und Verstehen des dynamischen Verhaltens.
LernzielKennenlernen von und praktische Anwendung von Mess- und Analysemethoden zur Ermittlung von Übertragungsfunktionen mechanischen Strukturen. Auswertung und Aufbereitung der Messdaten zum Visualisieren und Verstehen des dynamischen Verhaltens.
InhaltUmgang mit Beschleunigungs– und Kraftaufnehmern, Messung von Übertragungsfunktionen mechanischer Strukturen, Bestimmung und Darstellung der Schwingungsformen anhand praktischer Beispiele, Einführung in die Schwingungslehre und deren Grundbegriffe, diskrete Schwinger
Skriptja, Abgabe im Kurs (20.- CHF)
LiteraturDavid Ewins, Modal Testing: Theory and Practice
Voraussetzungen / BesonderesIm praktischen Teil des Kurses werden die Teilnehmer selber Messungen an Strukturen durchführen und diese anschliessen bezüglich Eigenfrequenzen und Schwingungsformen analysieren.
Maschineningenieurwissenschaften Master Information
Kernfächer
Energy, Flows and Processes
Die unter der Kategorie “Kernfächer” gelisteten Fächer sind empfohlen. Andere Kurse sind nicht ausgeschlossen, benötigen jedoch die Zustimmung des Tutors/der Tutorin.
NummerTitelTypECTSUmfangDozierende
101-0187-00LStructural Reliability and Risk Analysis Information W3 KP2GS. Marelli
KurzbeschreibungStructural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.
LernzielThe goal of this course is to provide the students with a thorough understanding of the key concepts behind structural reliability and risk analysis. After this course the students will have refreshed their knowledge of probability theory and statistics to model uncertainties in view of engineering applications. They will be able to analyze the reliability of a structure and to use risk assessment methods for decision making under uncertain conditions. They will be aware of the state-of-the-art computational methods and software in this field.
InhaltEngineers are confronted every day to decision making under limited amount of information and uncertain conditions. When designing new structures and systems, the design codes such as SIA or Euro- codes usually provide a framework that guarantees safety and reliability. However the level of safety is not quantified explicitly, which does not allow the analyst to properly choose between design variants and evaluate a total cost in case of failure. In contrast, the framework of risk analysis allows one to incorporate the uncertainty in decision making.

The first part of the course is a reminder on probability theory that is used as a main tool for reliability and risk analysis. Classical concepts such as random variables and vectors, dependence and correlation are recalled. Basic statistical inference methods used for building a probabilistic model from the available data, e.g. the maximum likelihood method, are presented.

The second part is related to structural reliability analysis, i.e. methods that allow one to compute probabilities of failure of a given system with respect to prescribed criteria. The framework of reliability analysis is first set up. Reliability indices are introduced together with the first order-second moment method (FOSM) and the first order reliability method (FORM). Methods based on Monte Carlo simulation are then reviewed and illustrated through various examples. By-products of reliability analysis such as sensitivity measures and partial safety coefficients are derived and their links to structural design codes is shown. The reliability of structural systems is also introduced as well as the methods used to reassess existing structures based on new information.

The third part of the course addresses risk assessment methods. Techniques for the identification of hazard scenarios and their representation by fault trees and event trees are described. Risk is defined with respect to the concept of expected utility in the framework of decision making. Elements of Bayesian decision making, i.e. pre-, post and pre-post risk assessment methods are presented.

The course also includes a tutorial using the UQLab software dedicated to real world structural reliability analysis.
SkriptSlides of the lectures are available online every week. A printed version of the full set of slides is proposed to the students at the beginning of the semester.
LiteraturAng, A. and Tang, W.H, Probability Concepts in Engineering - Emphasis on Applications to Civil and Environmental Engineering, 2nd Edition, John Wiley & Sons, 2007.

S. Marelli, R. Schöbi, B. Sudret, UQLab user manual - Structural reliability (rare events estimation), Report UQLab-V0.92-107.
Voraussetzungen / BesonderesBasic course on probability theory and statistics
Mechanics, Materials, Structures
Die unter der Kategorie “Kernfächer” gelisteten Fächer sind empfohlen. Andere Kurse sind nicht ausgeschlossen, benötigen jedoch die Zustimmung des Tutors/der Tutorin.
NummerTitelTypECTSUmfangDozierende
151-0360-00LMethoden der StrukturanalyseW4 KP2V + 1UG. Kress
KurzbeschreibungDie Grundlagen der Strukturauslegung werden nach den Kriterien der Festigkeit, der Stabilität, der Ermüdungsauslegung und der elasto-plastischen Strukturanalyse behandelt.
Strukturtheorien (für eindimensionalen und zweidimensionalen Tragwerke) werden auf der Basis der Energie sätze präsentiert.
LernzielErweiterung der Grundlagen zur Behandlung strukturmechanischer Auslegungsproblemen. Einführung in die Dimensionierung von Flächentragwerke. Verständnis des Zusammenhangs zwischen Materialverhalten, Strukturtheorien und Auslegungskriterien.
Inhalt1. Grundproblem der Kontinuumsmechanik und Energiesätze: Herleitung von Strukturtheorien; Homogenisierungstheorien; Finite Elementen; Bruchmechanik.
2. Strukturtheorien für Flächentragwerke und Stabilität: Scheiben, Platten; Beulen von Platten (nichtlineare Plattentheorie)
3. Festigkeitshypothesen und Materialverhalten: Duktiles Verhalten, Plastizität, vMises, Tresca, Hauptspannungshypothese; Sprödes Verhalten; Viskoplastisches Verhalten, Kriechfestigkeit
4. Strukturauslegung: Ermüdung und dynamische Strukturanalyse
SkriptSkript und alle anderen Vorlesungsunterlagen erhältlich auf MOODLE
Voraussetzungen / Besondereskeine
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Robotics, Systems and Control
Die unter der Kategorie “Kernfächer” gelisteten Fächer sind empfohlen. Andere Kurse sind nicht ausgeschlossen, benötigen jedoch die Zustimmung des Tutors/der Tutorin.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Micro & Nanosystems
Die unter der Kategorie “Kernfächer” gelisteten Fächer sind empfohlen. Andere Kurse sind nicht ausgeschlossen, benötigen jedoch die Zustimmung des Tutors/der Tutorin.
NummerTitelTypECTSUmfangDozierende
227-0377-00LPhysics of Failure and Failure Analysis of Electronic Devices and EquipmentW3 KP2VU. Sennhauser
KurzbeschreibungFailures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
LernzielIntroduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
SkriptComprehensive copy of transparencies
Bioengineering
Die unter der Kategorie “Kernfächer” gelisteten Fächer sind empfohlen. Andere Kurse sind nicht ausgeschlossen, benötigen jedoch die Zustimmung des Tutors/der Tutorin.
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
406-0353-AALAnalysis III Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RF. Da Lio
KurzbeschreibungEinführung in die partiellen Differentialgleichungen. Klassifizieren und Lösen von in der Praxis wichtigen Differentialgleichungen. Es werden elliptische, parabolische und hyperbolische Differentialgleichungen behandelt. Folgende mathematischen Techniken werden vorgestellt: Laplacetransformation, Fourierreihen, Separation der Variablen, Methode der Charakteristiken.
LernzielMathematische Behandlung naturwissenschaftlicher Probleme lernen. Verstehen der Eigenschaften der verschiedenen Typen von partiellen Differentialgleichungen.
InhaltLaplace Transforms:
- Laplace Transform, Inverse Laplace Transform, Linearity, s-Shifting
- Transforms of Derivatives and Integrals, ODEs
- Unit Step Function, t-Shifting
- Short Impulses, Dirac's Delta Function, Partial Fractions
- Convolution, Integral Equations
- Differentiation and Integration of Transforms

Fourier Series, Integrals and Transforms:
- Fourier Series
- Functions of Any Period p=2L
- Even and Odd Functions, Half-Range Expansions
- Forced Oscillations
- Approximation by Trigonometric Polynomials
- Fourier Integral
- Fourier Cosine and Sine Transform

Partial Differential Equations:
- Basic Concepts
- Modeling: Vibrating String, Wave Equation
- Solution by separation of variables; use of Fourier series
- D'Alembert Solution of Wave Equation, Characteristics
- Heat Equation: Solution by Fourier Series
- Heat Equation: Solutions by Fourier Integrals and Transforms
- Modeling Membrane: Two Dimensional Wave Equation
- Laplacian in Polar Coordinates: Circular Membrane, Fourier-Bessel Series
- Solution of PDEs by Laplace Transform
LiteraturE. Kreyszig, Advanced Engineering Mathematics, John Wiley & Sons, 10. Auflage, 2011

C. R. Wylie & L. Barrett, Advanced Engineering Mathematics, McGraw-Hill, 6th ed.
Stanley J. Farlow, Partial Differential Equations for Scientists and Engineers, (Dover Books on Mathematics).

G. Felder, Partielle Differenzialgleichungen für Ingenieurinnen und Ingenieure, hypertextuelle Notizen zur Vorlesung Analysis III im WS 2002/2003.

Y. Pinchover, J. Rubinstein, An Introduction to Partial Differential Equations, Cambridge University Press, 2005

For reference/complement of the Analysis I/II courses:

Christian Blatter: Ingenieur-Analysis (Download PDF)
Voraussetzungen / BesonderesWeitere Informationen unter:
http://www.math.ethz.ch/education/bachelor/lectures/hs2013/other/analysis3_itet
Materialwissenschaft Bachelor Information
1. Semester
Grundlagenfächer Teil 1
Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0261-GULAnalysis I Information O8 KP5V + 3UA. Steiger
KurzbeschreibungDifferential- und Integralrechnung von Funktionen einer und mehrerer Variablen; Vektoranalysis; gewöhnliche Differentialgleichungen erster und höherer Ordnung, Differentialgleichungssysteme; Potenzreihen. In jedem Teilbereich eine grosse Anzahl von Anwendungsbeispielen aus Mechanik, Physik und anderen Lehrgebieten des Ingenieurstudiums.
LernzielEinführung in die mathematischen Grundlagen der Ingenieurwissenschaften, soweit sie die Differential- und Integralrechnung betreffen.
LiteraturU. Stammbach: Analysis I/II, Teil A, B, C und Aufgabensammlung

Die Vorlesung folgt dem Skript von Prof. U. Stammbach. Die vier Bände sind im Gesamtpaket zum Spezialpreis von CHF 75.- nur im ETH Store erhältlich und sehr zu empfehlen. Es findet kein Hörsaalverkauf statt.
Voraussetzungen / BesonderesDie Übungsaufgaben (inkl. Multiple Choice) sind ein wichtiger Bestandteil der Lehrveranstaltung. Es wird erwartet, dass Sie mindestens 75% der wöchentlichen Serien bearbeiten und zur Korrektur einreichen.
3. Semester
Grundlagenfächer Teil 2
Prüfungsblock 2
NummerTitelTypECTSUmfangDozierende
401-0363-10LAnalysis IIIO3 KP2V + 1UF. Da Lio
KurzbeschreibungIntroduction to partial differential equations. Differential equations which are important in applications are classified and solved. Elliptic, parabolic and hyperbolic differential equations are treated. The following mathematical tools are introduced: Laplace transforms, Fourier series, separation of variables, methods of characteristics.
LernzielMathematical treatment of problems in science and engineering. To understand the properties of the different types of partial differential equations.

The first lecture is on Thursday, September 28 13-15 in HG F 7 and video transmitted into HG F 5.

The coordinator is Simon Brun
https://www.math.ethz.ch/the-department/people.html?u=brunsi
InhaltLaplace Transforms:
- Laplace Transform, Inverse Laplace Transform, Linearity, s-Shifting
- Transforms of Derivatives and Integrals, ODEs
- Unit Step Function, t-Shifting
- Short Impulses, Dirac's Delta Function, Partial Fractions
- Convolution, Integral Equations
- Differentiation and Integration of Transforms

Fourier Series, Integrals and Transforms:
- Fourier Series
- Functions of Any Period p=2L
- Even and Odd Functions, Half-Range Expansions
- Forced Oscillations
- Approximation by Trigonometric Polynomials
- Fourier Integral
- Fourier Cosine and Sine Transform

Partial Differential Equations:
- Basic Concepts
- Modeling: Vibrating String, Wave Equation
- Solution by separation of variables; use of Fourier series
- D'Alembert Solution of Wave Equation, Characteristics
- Heat Equation: Solution by Fourier Series
- Heat Equation: Solutions by Fourier Integrals and Transforms
- Modeling Membrane: Two Dimensional Wave Equation
- Laplacian in Polar Coordinates: Circular Membrane, Fourier-Bessel Series
- Solution of PDEs by Laplace Transform
SkriptLecture notes by Prof. Dr. Alessandra Iozzi:
https://polybox.ethz.ch/index.php/s/D3K0TayQXvfpCAA
LiteraturE. Kreyszig, Advanced Engineering Mathematics, John Wiley & Sons, 10. Auflage, 2011

C. R. Wylie & L. Barrett, Advanced Engineering Mathematics, McGraw-Hill, 6th ed.

S.J. Farlow, Partial Differential Equations for Scientists and Engineers, Dover Books on Mathematics, NY.

G. Felder, Partielle Differenzialgleichungen für Ingenieurinnen und Ingenieure, hypertextuelle Notizen zur Vorlesung Analysis III im WS 2002/2003.

Y. Pinchover, J. Rubinstein, An Introduction to Partial Differential Equations, Cambridge University Press, 2005

For reference/complement of the Analysis I/II courses:

Christian Blatter: Ingenieur-Analysis
https://people.math.ethz.ch/~blatter/dlp.html
Mathematik Bachelor Information
Obligatorische Fächer des Basisjahres
Basisprüfungsblock 2
NummerTitelTypECTSUmfangDozierende
401-1261-07LAnalysis I Information O10 KP6V + 3UM. Einsiedler
KurzbeschreibungEinführung in die Differential- und Integralrechnung in einer reellen Veränderlichen: Grundbegriffe des mathematischen Denkens, Zahlen, Folgen und Reihen, topologische Grundbegriffe, stetige Funktionen, differenzierbare Funktionen, gewöhnliche Differentialgleichungen, Riemannsche Integration.
LernzielMathematisch exakter Umgang mit Grundbegriffen der Differential-und Integralrechnung.
LiteraturH. Amann, J. Escher: Analysis I
https://link.springer.com/book/10.1007/978-3-7643-7756-4

J. Appell: Analysis in Beispielen und Gegenbeispielen
https://link.springer.com/book/10.1007/978-3-540-88903-8

R. Courant: Vorlesungen über Differential- und Integralrechnung
https://link.springer.com/book/10.1007/978-3-642-61988-5

O. Forster: Analysis 1
https://link.springer.com/book/10.1007/978-3-658-00317-3

H. Heuser: Lehrbuch der Analysis
https://link.springer.com/book/10.1007/978-3-322-96828-9

K. Königsberger: Analysis 1
https://link.springer.com/book/10.1007/978-3-642-18490-1

W. Walter: Analysis 1
https://link.springer.com/book/10.1007/3-540-35078-0

V. Zorich: Mathematical Analysis I (englisch)
https://link.springer.com/book/10.1007/978-3-662-48792-1

A. Beutelspacher: "Das ist o.B.d.A. trivial"
https://link.springer.com/book/10.1007/978-3-8348-9599-8

H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten
https://link.springer.com/book/10.1007/978-3-642-28646-9
Obligatorische Fächer
Prüfungsblock I
Im Prüfungsblock I muss entweder die Lerneinheit 402-2883-00L Physik III oder die Lerneinheit 402-2203-01L Allgemeine Mechanik gewählt und zur Prüfung angemeldet werden. (Die andere der beiden Lerneinheiten kann im ETH Bachelor-Studiengang Mathematik belegt, aber weder in myStudies zur Prüfung angemeldet noch für den Studiengang angerechnet werden.)
NummerTitelTypECTSUmfangDozierende
401-2303-00LFunktionentheorie Information O6 KP3V + 2UR. Pandharipande
KurzbeschreibungKomplexe Funktionen einer komplexen Veränderlichen, Cauchy-Riemann Gleichungen, Cauchyscher Integralsatz, Singularitäten, Residuensatz, Umlaufzahl, analytische Fortsetzung, spezielle Funktionen, konforme Abbildungen. Riemannscher Abbildungssatz.
LernzielFähigkeit zum Umgang mit analytischen Funktion; insbesondre Anwendungen des Residuensatzes
LiteraturTh. Gamelin: Complex Analysis. Springer 2001

E. Titchmarsh: The Theory of Functions. Oxford University Press

D. Salamon: "Funktionentheorie". Birkhauser, 2011. (In German)

L. Ahlfors: "Complex analysis. An introduction to the theory of analytic functions of one complex variable." International Series in Pure and Applied Mathematics. McGraw-Hill Book Co.

B. Palka: "An introduction to complex function theory."
Undergraduate Texts in Mathematics. Springer-Verlag, 1991.

K.Jaenich: Funktionentheorie. Springer Verlag

R.Remmert: Funktionentheorie I. Springer Verlag

E.Hille: Analytic Function Theory. AMS Chelsea Publications
Kernfächer
Kernfächer aus Bereichen der reinen Mathematik
NummerTitelTypECTSUmfangDozierende
401-3461-00LFunctional Analysis I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Voraussetzungen / BesonderesSolid background on the content of all Mathematics courses of the first two years of the undergraduate curriculum at ETH (most remarkably: fluency with measure theory, Lebesgue integration and L^p spaces).
Wahlfächer
Auswahl: Wahrscheinlichkeitstheorie, Statistik
NummerTitelTypECTSUmfangDozierende
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Seminare
Bitte Seminare frühzeitig im myStudies belegen, damit wir einen allfälligen Bedarf an weiteren Seminaren rechtzeitig erkennen. Bei einigen Seminaren werden Wartelisten geführt. Belegen Sie trotzdem höchstens zwei Mathematik-Seminare. In diesem Fall bekunden Sie für das Seminar, das Sie zuerst belegen, eine höhere Präferenz.
NummerTitelTypECTSUmfangDozierende
401-3680-67LPersistent Homology and Topological Data Analysis Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 8
W4 KP2SP. S. Jossen
KurzbeschreibungWe study the fundamental tools of topological data analysis: Persistent homology, persistence modules and barcodes. Our goal is to read and understand parts of the paper "Principal Component Analysis of Persistent Homology..." by Vanessa Robins and Kate Turner (ArXiV 1507.01454v1).
LernzielTo get familiar with the basic concepts of topological data analysis and see some applications thereof.
LiteraturHerbert Edelsbrunner and John L. Harer: Computational Topology, An Introduction. AMS 2010
Voraussetzungen / BesonderesParticipants are supposed to be familiar with singular homology.
401-3650-67LNumerical Analysis Seminar: Tensor Numerics and Deep Neural Networks Information Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 10
W4 KP2SC. Schwab
KurzbeschreibungThe seminar addresses recently discovered
_mathematical_ connections between Deep Learning
and Tensor-formatted numerical analysis,
with particular attention to
the numerical solution of partial differential equations,
with random input data.
LernzielThe aim of the seminar is to review recent [2015-]
research work and results,
together with recently published software
such as the TT-Toolbox, and Google's TENSORFLOW.

The focus is on the mathematical analysis and
interpretation of current learning approaches
and related mathematical and technical fields, e.g.
high-dimensional approximation, tensor structured numerical methods
for the numerical solution of highdimensional PDEs,
with applications in computational UQ.
For theory, we refer to the references in the survey
https://sinews.siam.org/Details-Page/deep-deep-trouble
Numerical experiments will be done with TENSORFLOW and with the
TT toolbox at
https://github.com/oseledets/TT-Toolbox
SkriptThe seminar will study a set of 10 orginial papers from 2015 to today.
LiteraturHelmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
Optimal Approximation with Sparsely Connected Deep Neural Networks
arXiv:1705.01714

N. Cohen, O. Sharir, Y. Levine, R. Tamari, D. Yakira and A. Shashua (May 2017):
Analysis and design of convolutional networks via hierarchical tensor decompositions,
arXiv:1705.02302v3.

N. Cohen and A. Shashua (March 2016),
Convolutional rectifier networks as generalized tensor decompositions,
Technical report, arXiv:1603.00162.
Proceedings of The 33rd International Conference on Machine Learning, pp. 955-963, 2016.

N. Cohen, O. Sharir and A. Shashua (Sept. 2015),
On the expressive power of deep learning: A tensor analysis,
Technical report, arXiv:1509.05009.
Journal-ref: 29th Annual Conference on Learning Theory, pp. 698-728, 2016.
Voraussetzungen / BesonderesCompleted BSc MATH exam.
Mathematik Master Information
Kernfächer
Für das Master-Diplom in Angewandter Mathematik ist die folgende Zusatzbedingung (nicht in myStudies ersichtlich) zu beachten: Mindestens 15 KP der erforderlichen 28 KP aus Kern- und Wahlfächern müssen aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten stammen.
Bachelor-Kernfächer aus Bereichen der reinen Mathematik
Nebst weiteren Einschränkungen gilt:
Die Anrechnung von 401-3531-00L Differentialgeometrie I / Differential Geometry I im Master-Studiengang ist nur dann zulässig, wenn 401-3532-00L Differentialgeometrie II / Differential Geometry II nicht für den Bachelor-Studiengang angerechnet wurde.
Ebenso für:
401-3461-00L Funktionalanalysis I / Functional Analysis I - 401-3462-00L Funktionalanalysis II / Functional Analysis II
401-3001-61L Algebraische Topologie I / Algebraic Topology I - 401-3002-12L Algebraische Topologie II / Algebraic Topology II
401-3132-00L Kommutative Algebra / Commutative Algebra - 401-3146-12L Algebraische Geometrie / Algebraic Geometry
401-3371-00L Dynamische Systeme I / Dynamical Systems I - 401-3372-00L Dynamische Systeme II / Dynamical Systems II
Wenden Sie sich für die Kategoriezuordnung nach dem Verfügen des Prüfungsresultates an das Studiensekretariat (www.math.ethz.ch/studiensekretariat).
NummerTitelTypECTSUmfangDozierende
401-3461-00LFunctional Analysis I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
E-10 KP4V + 1UA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Voraussetzungen / BesonderesSolid background on the content of all Mathematics courses of the first two years of the undergraduate curriculum at ETH (most remarkably: fluency with measure theory, Lebesgue integration and L^p spaces).
Wahlfächer
Für das Master-Diplom in Angewandter Mathematik ist die folgende Zusatzbedingung (nicht in myStudies ersichtlich) zu beachten: Mindestens 15 KP der erforderlichen 28 KP aus Kern- und Wahlfächern müssen aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten stammen.
Wahlfächer aus Bereichen der angewandten Mathematik ...
vollständiger Titel:
Wahlfächer aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten
Auswahl: Numerische Mathematik
NummerTitelTypECTSUmfangDozierende
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UA. Jentzen
KurzbeschreibungCourse on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.
LernzielThe aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.
InhaltGeneration of random numbers
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
SkriptLecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).
LiteraturP. Glassermann:
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.

P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
Voraussetzungen / BesonderesPrerequisites:

Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.

a) mandatory courses:
Elementary Probability,
Probability Theory I.

b) recommended courses:
Stochastic Processes.

Start of lectures: Wednesday, September 20, 2017

Date of the End-of-Semester examination: Wednesday, December 20, 2017, 13:00-15:00; students must arrive before 12:30 at ETH HG E 19.
Room for the End-of-Semester examination: ETH HG E 19.

Exam inspection: Monday, March 5, 2018,
13:00-14:00 at HG D 5.1
Please bring your legi.
Auswahl: Wahrscheinlichkeitstheorie, Statistik
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
Anwendungsgebiet
Nur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar.
In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden.
Image Processing and Computer Vision
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
Seminare und Semesterarbeiten
Seminare
Bitte Seminare frühzeitig im myStudies belegen, damit wir einen allfälligen Bedarf an weiteren Seminaren rechtzeitig erkennen. Bei einigen Seminaren werden Wartelisten geführt. Belegen Sie trotzdem höchstens zwei Mathematik-Seminare. In diesem Fall bekunden Sie für das Seminar, das Sie zuerst belegen, eine höhere Präferenz.
NummerTitelTypECTSUmfangDozierende
401-3680-67LPersistent Homology and Topological Data Analysis Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 8
W4 KP2SP. S. Jossen
KurzbeschreibungWe study the fundamental tools of topological data analysis: Persistent homology, persistence modules and barcodes. Our goal is to read and understand parts of the paper "Principal Component Analysis of Persistent Homology..." by Vanessa Robins and Kate Turner (ArXiV 1507.01454v1).
LernzielTo get familiar with the basic concepts of topological data analysis and see some applications thereof.
LiteraturHerbert Edelsbrunner and John L. Harer: Computational Topology, An Introduction. AMS 2010
Voraussetzungen / BesonderesParticipants are supposed to be familiar with singular homology.
401-3650-67LNumerical Analysis Seminar: Tensor Numerics and Deep Neural Networks Information Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 10
W4 KP2SC. Schwab
KurzbeschreibungThe seminar addresses recently discovered
_mathematical_ connections between Deep Learning
and Tensor-formatted numerical analysis,
with particular attention to
the numerical solution of partial differential equations,
with random input data.
LernzielThe aim of the seminar is to review recent [2015-]
research work and results,
together with recently published software
such as the TT-Toolbox, and Google's TENSORFLOW.

The focus is on the mathematical analysis and
interpretation of current learning approaches
and related mathematical and technical fields, e.g.
high-dimensional approximation, tensor structured numerical methods
for the numerical solution of highdimensional PDEs,
with applications in computational UQ.
For theory, we refer to the references in the survey
https://sinews.siam.org/Details-Page/deep-deep-trouble
Numerical experiments will be done with TENSORFLOW and with the
TT toolbox at
https://github.com/oseledets/TT-Toolbox
SkriptThe seminar will study a set of 10 orginial papers from 2015 to today.
LiteraturHelmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
Optimal Approximation with Sparsely Connected Deep Neural Networks
arXiv:1705.01714

N. Cohen, O. Sharir, Y. Levine, R. Tamari, D. Yakira and A. Shashua (May 2017):
Analysis and design of convolutional networks via hierarchical tensor decompositions,
arXiv:1705.02302v3.

N. Cohen and A. Shashua (March 2016),
Convolutional rectifier networks as generalized tensor decompositions,
Technical report, arXiv:1603.00162.
Proceedings of The 33rd International Conference on Machine Learning, pp. 955-963, 2016.

N. Cohen, O. Sharir and A. Shashua (Sept. 2015),
On the expressive power of deep learning: A tensor analysis,
Technical report, arXiv:1509.05009.
Journal-ref: 29th Annual Conference on Learning Theory, pp. 698-728, 2016.
Voraussetzungen / BesonderesCompleted BSc MATH exam.
Zusätzliche Veranstaltungen
NummerTitelTypECTSUmfangDozierende
401-5350-00LAnalysis Seminar Information E-0 KP1KM. Struwe, A. Carlotto, F. Da Lio, A. Figalli, N. Hungerbühler, T. Ilmanen, T. Kappeler, T. Rivière, D. A. Salamon
KurzbeschreibungResearch colloquium
Lernziel
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
406-2303-AALComplex Analysis Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-6 KP13RR. Pandharipande
KurzbeschreibungComplex functions of one variable, Cauchy-Riemann equations, Cauchy theorem and integral formula, singularities, residue theorem, index of closed curves, analytic continuation, conformal mappings, Riemann mapping theorem.
Lernziel
LiteraturL. Ahlfors: "Complex analysis. An introduction to the theory of analytic functions of one complex variable." International Series in Pure and Applied Mathematics. McGraw-Hill Book Co.

B. Palka: "An introduction to complex function theory."
Undergraduate Texts in Mathematics. Springer-Verlag, 1991.

R.Remmert: Theory of Complex Functions.. Springer Verlag

E.Hille: Analytic Function Theory. AMS Chelsea Publication
406-3461-AALFunctional Analysis I
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-10 KP21RA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe.
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Mikro- und Nanosysteme Master Information
Kernfächer
Wählbare Kernfächer
NummerTitelTypECTSUmfangDozierende
227-0377-00LPhysics of Failure and Failure Analysis of Electronic Devices and EquipmentW3 KP2VU. Sennhauser
KurzbeschreibungFailures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.
LernzielIntroduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis
SkriptComprehensive copy of transparencies
Neural Systems and Computation Master Information
Kernfächer
Obligatorische Kernfächer
NummerTitelTypECTSUmfangDozierende
227-1039-00LBasics of Instrumentation, Measurement, and Analysis (University of Zurich) Belegung eingeschränkt - Details anzeigen
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI502

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html

Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots.
Preference is given to students that require this class as part of their major.
O4 KP9SS.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, W. von der Behrens
KurzbeschreibungExperimental data are always as good as the instrumentation and measurement, but never any better. This course provides the very basics of instrumentation relevant to neurophysiology and neuromorphic engineering, it consists of two parts: a common introductory part involving analog signals and their acquisition (Part I), and a more specialized second part (Part II).
LernzielThe goal of Part I is to provide a general introduction to the signal acquisition process. Students are familiarized with basic lab equipment such as oscilloscopes, function generators, and data acquisition devices. Different electrical signals are generated, visualized, filtered, digitized, and analyzed using Matlab (Mathworks Inc.) or Labview (National Instruments).

In Part II, the students are divided into small groups to work on individual measurement projects according to availability and interest. Students single-handedly solve a measurement task, making use of their basic knowledge acquired in the first part. Various signal sources will be provided.
Voraussetzungen / BesonderesFor each part, students must hand in a written report and present a live demonstration of their measurement setup to the respective supervisor. The supervisor of Part I is the teaching assistant, and the supervisor of Part II is task specific. Admission to Part II is conditional on completion of Part I (report + live demonstration).

Reports must contain detailed descriptions of the measurement goal, the measurement procedure, and the measurement outcome. Either confidence or significance of measurements must be provided. Acquisition and analysis software must be documented.
Physik Bachelor Information
Obligatorische Fächer des Basisjahres
Basisprüfungsblock 2
NummerTitelTypECTSUmfangDozierende
401-1261-07LAnalysis I Information O10 KP6V + 3UM. Einsiedler
KurzbeschreibungEinführung in die Differential- und Integralrechnung in einer reellen Veränderlichen: Grundbegriffe des mathematischen Denkens, Zahlen, Folgen und Reihen, topologische Grundbegriffe, stetige Funktionen, differenzierbare Funktionen, gewöhnliche Differentialgleichungen, Riemannsche Integration.
LernzielMathematisch exakter Umgang mit Grundbegriffen der Differential-und Integralrechnung.
LiteraturH. Amann, J. Escher: Analysis I
https://link.springer.com/book/10.1007/978-3-7643-7756-4

J. Appell: Analysis in Beispielen und Gegenbeispielen
https://link.springer.com/book/10.1007/978-3-540-88903-8

R. Courant: Vorlesungen über Differential- und Integralrechnung
https://link.springer.com/book/10.1007/978-3-642-61988-5

O. Forster: Analysis 1
https://link.springer.com/book/10.1007/978-3-658-00317-3

H. Heuser: Lehrbuch der Analysis
https://link.springer.com/book/10.1007/978-3-322-96828-9

K. Königsberger: Analysis 1
https://link.springer.com/book/10.1007/978-3-642-18490-1

W. Walter: Analysis 1
https://link.springer.com/book/10.1007/3-540-35078-0

V. Zorich: Mathematical Analysis I (englisch)
https://link.springer.com/book/10.1007/978-3-662-48792-1

A. Beutelspacher: "Das ist o.B.d.A. trivial"
https://link.springer.com/book/10.1007/978-3-8348-9599-8

H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten
https://link.springer.com/book/10.1007/978-3-642-28646-9
Obligatorische Fächer
Obligatorische Fächer des zweiten Studienjahres
Prüfungsblock I
NummerTitelTypECTSUmfangDozierende
401-2303-00LFunktionentheorie Information O6 KP3V + 2UR. Pandharipande
KurzbeschreibungKomplexe Funktionen einer komplexen Veränderlichen, Cauchy-Riemann Gleichungen, Cauchyscher Integralsatz, Singularitäten, Residuensatz, Umlaufzahl, analytische Fortsetzung, spezielle Funktionen, konforme Abbildungen. Riemannscher Abbildungssatz.
LernzielFähigkeit zum Umgang mit analytischen Funktion; insbesondre Anwendungen des Residuensatzes
LiteraturTh. Gamelin: Complex Analysis. Springer 2001

E. Titchmarsh: The Theory of Functions. Oxford University Press

D. Salamon: "Funktionentheorie". Birkhauser, 2011. (In German)

L. Ahlfors: "Complex analysis. An introduction to the theory of analytic functions of one complex variable." International Series in Pure and Applied Mathematics. McGraw-Hill Book Co.

B. Palka: "An introduction to complex function theory."
Undergraduate Texts in Mathematics. Springer-Verlag, 1991.

K.Jaenich: Funktionentheorie. Springer Verlag

R.Remmert: Funktionentheorie I. Springer Verlag

E.Hille: Analytic Function Theory. AMS Chelsea Publications
Auswahl an Lehrveranstaltungen aus höheren Semestern
NummerTitelTypECTSUmfangDozierende
401-3461-00LFunctional Analysis I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Voraussetzungen / BesonderesSolid background on the content of all Mathematics courses of the first two years of the undergraduate curriculum at ETH (most remarkably: fluency with measure theory, Lebesgue integration and L^p spaces).
Physik Master Information
Wahlfächer
Physikalische und mathematische Wahlfächer
Auswahl: Mathematik
NummerTitelTypECTSUmfangDozierende
401-3461-00LFunctional Analysis I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Voraussetzungen / BesonderesSolid background on the content of all Mathematics courses of the first two years of the undergraduate curriculum at ETH (most remarkably: fluency with measure theory, Lebesgue integration and L^p spaces).
Quantitative Finance Master Information
siehe www.msfinance.ch/index.html?/portrait/Curriculum.html

Studierende im Joint Degree Master-Studiengang "Quantitative Finance" müssen Module der Universität Zürich direkt an der Universität Zürich buchen. Die entsprechenden Module sind hier nicht aufgelistet.
Wahlfplichtmodule
Bereich MF (Mathematical Methods for Finance)
Für allfällige weitere Kursangebote siehe www.msfinance.ch
NummerTitelTypECTSUmfangDozierende
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UA. Jentzen
KurzbeschreibungCourse on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.
LernzielThe aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.
InhaltGeneration of random numbers
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
SkriptLecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).
LiteraturP. Glassermann:
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.

P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
Voraussetzungen / BesonderesPrerequisites:

Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.

a) mandatory courses:
Elementary Probability,
Probability Theory I.

b) recommended courses:
Stochastic Processes.

Start of lectures: Wednesday, September 20, 2017

Date of the End-of-Semester examination: Wednesday, December 20, 2017, 13:00-15:00; students must arrive before 12:30 at ETH HG E 19.
Room for the End-of-Semester examination: ETH HG E 19.

Exam inspection: Monday, March 5, 2018,
13:00-14:00 at HG D 5.1
Please bring your legi.
Raumentwicklung und Infrastruktursysteme Master Information
1. Semester
Vertiefungsfächer
Vertiefung in Raum- und Landschaftsentwicklung
NummerTitelTypECTSUmfangDozierende
103-0307-00LMultikriterielle Entscheidungsanalyse Information W3 KP2GA. Grêt-Regamey
KurzbeschreibungPlaner müssen Entscheidungen über optimale Landnutzungen und ihre räumliche Anordnung treffen. Dank erhöhter Verfügbarkeit räumlicher Daten und GIS-Analysefertigkeiten werden für die Planung wirksamere Entscheidungsunterstützungssysteme entwickelt. Im Kurs werden die Grundlagen räumlicher Analysen sowie die Integration räumlicher Daten in multikriterielle Entscheidungssysteme vermittelt.
LernzielDer Kurs soll:
1) Studierende in Techniken und Belange der räumlichen Entscheidungsunterstützungssystemen einführen, inklusive Analysetechniken
2) praktische Übungen dieser Ansätze mit R anbieten, welche reale Umwelt- und Landschaftsplanungsprobleme betreffen.

Der Fokus liegt auf Konzepten, Datenressourcen, und Analyseinstrumenten, welche Studierende in einer wissenschaftlichen Karriere oder in der Praxis einsetzen können.
SkriptDie Unterlagen, bestehend aus Präsentationsunterlagen der einzelnen Referate und einem Skript werden teilweise abgegeben und stehen auf der Homepage des Fachbereichs PLUS zum Download bereit.

Download: http://www.irl.ethz.ch/plus/education
Voraussetzungen / BesonderesDer Kurs setzt Grundkenntnisse von R Software voraus. RE&IS-Masterstudierende bekommen dies in der Lerneinheit "Basics of RE&IS" (103-0377-10L) vermittelt. Vorausgesetzt, dass es noch freie Plätze gibt, ist diese Lerneinheit auch für Studierende anderer Studiengänge offen (d.h. erste fünf Lektionen, ohne Vergabe von Kreditpunkten). Solche Studierenden können sich via Email bei Maarten van Strien (vanstrien@ethz.ch) anmelden. Alternativ können die Grundlagen zu R über Online-Tutorials, wie z.B. "Introduction to R" by W. N. Venables and D. M. Smith available online at http://cran.r-project.org/doc/manuals/R-intro.pdf erworben werden.
Netzinfrastrukturen
NummerTitelTypECTSUmfangDozierende
101-0187-00LStructural Reliability and Risk Analysis Information W3 KP2GS. Marelli
KurzbeschreibungStructural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.
LernzielThe goal of this course is to provide the students with a thorough understanding of the key concepts behind structural reliability and risk analysis. After this course the students will have refreshed their knowledge of probability theory and statistics to model uncertainties in view of engineering applications. They will be able to analyze the reliability of a structure and to use risk assessment methods for decision making under uncertain conditions. They will be aware of the state-of-the-art computational methods and software in this field.
InhaltEngineers are confronted every day to decision making under limited amount of information and uncertain conditions. When designing new structures and systems, the design codes such as SIA or Euro- codes usually provide a framework that guarantees safety and reliability. However the level of safety is not quantified explicitly, which does not allow the analyst to properly choose between design variants and evaluate a total cost in case of failure. In contrast, the framework of risk analysis allows one to incorporate the uncertainty in decision making.

The first part of the course is a reminder on probability theory that is used as a main tool for reliability and risk analysis. Classical concepts such as random variables and vectors, dependence and correlation are recalled. Basic statistical inference methods used for building a probabilistic model from the available data, e.g. the maximum likelihood method, are presented.

The second part is related to structural reliability analysis, i.e. methods that allow one to compute probabilities of failure of a given system with respect to prescribed criteria. The framework of reliability analysis is first set up. Reliability indices are introduced together with the first order-second moment method (FOSM) and the first order reliability method (FORM). Methods based on Monte Carlo simulation are then reviewed and illustrated through various examples. By-products of reliability analysis such as sensitivity measures and partial safety coefficients are derived and their links to structural design codes is shown. The reliability of structural systems is also introduced as well as the methods used to reassess existing structures based on new information.

The third part of the course addresses risk assessment methods. Techniques for the identification of hazard scenarios and their representation by fault trees and event trees are described. Risk is defined with respect to the concept of expected utility in the framework of decision making. Elements of Bayesian decision making, i.e. pre-, post and pre-post risk assessment methods are presented.

The course also includes a tutorial using the UQLab software dedicated to real world structural reliability analysis.
SkriptSlides of the lectures are available online every week. A printed version of the full set of slides is proposed to the students at the beginning of the semester.
LiteraturAng, A. and Tang, W.H, Probability Concepts in Engineering - Emphasis on Applications to Civil and Environmental Engineering, 2nd Edition, John Wiley & Sons, 2007.

S. Marelli, R. Schöbi, B. Sudret, UQLab user manual - Structural reliability (rare events estimation), Report UQLab-V0.92-107.
Voraussetzungen / BesonderesBasic course on probability theory and statistics
103-0307-00LMultikriterielle Entscheidungsanalyse Information W3 KP2GA. Grêt-Regamey
KurzbeschreibungPlaner müssen Entscheidungen über optimale Landnutzungen und ihre räumliche Anordnung treffen. Dank erhöhter Verfügbarkeit räumlicher Daten und GIS-Analysefertigkeiten werden für die Planung wirksamere Entscheidungsunterstützungssysteme entwickelt. Im Kurs werden die Grundlagen räumlicher Analysen sowie die Integration räumlicher Daten in multikriterielle Entscheidungssysteme vermittelt.
LernzielDer Kurs soll:
1) Studierende in Techniken und Belange der räumlichen Entscheidungsunterstützungssystemen einführen, inklusive Analysetechniken
2) praktische Übungen dieser Ansätze mit R anbieten, welche reale Umwelt- und Landschaftsplanungsprobleme betreffen.

Der Fokus liegt auf Konzepten, Datenressourcen, und Analyseinstrumenten, welche Studierende in einer wissenschaftlichen Karriere oder in der Praxis einsetzen können.
SkriptDie Unterlagen, bestehend aus Präsentationsunterlagen der einzelnen Referate und einem Skript werden teilweise abgegeben und stehen auf der Homepage des Fachbereichs PLUS zum Download bereit.

Download: http://www.irl.ethz.ch/plus/education
Voraussetzungen / BesonderesDer Kurs setzt Grundkenntnisse von R Software voraus. RE&IS-Masterstudierende bekommen dies in der Lerneinheit "Basics of RE&IS" (103-0377-10L) vermittelt. Vorausgesetzt, dass es noch freie Plätze gibt, ist diese Lerneinheit auch für Studierende anderer Studiengänge offen (d.h. erste fünf Lektionen, ohne Vergabe von Kreditpunkten). Solche Studierenden können sich via Email bei Maarten van Strien (vanstrien@ethz.ch) anmelden. Alternativ können die Grundlagen zu R über Online-Tutorials, wie z.B. "Introduction to R" by W. N. Venables and D. M. Smith available online at http://cran.r-project.org/doc/manuals/R-intro.pdf erworben werden.
Vertiefungsfächer für alle Vertiefungen
NummerTitelTypECTSUmfangDozierende
101-0439-00LIntroduction to Economic Analysis - A Case Study Approach with Cost Benefit Analysis in Transport
Remark:
Former Title "Introduction to Economic Policy - A Case Study Approach with Cost Benefit Analysis in Transport".
W6 KP4GK. W. Axhausen, R. Schubert
KurzbeschreibungDie Vorlesung stellt einige grundlegende ökonomische Prinzipien sowie die Verfahren der Kosten-Nutzen-Analyse vor; sie führt auch in Methoden zur Ermittlung von Bewertungsgrössen ein
LernzielSichere Kenntnis mikro- und makroökonomischer Grundlagen. Erarbeitung und Übung von Verfahren der Bewertung von Massnahmen und infrastrukturellen Ausbauten
InhaltMikro-und makroökonomische Grundlagen; Kosten - Nutzen - Analyse; Nutzwertanalyse; Europäische Richtlinien; Stated response Verfahren; Reisekostenansatz et al.; Bewertung von Reisezeitveränderungen; Bewertung der Verkehrssicherheit
Skriptmoodle Plattform für die ökonomischen Grundlagen; Umdrucke
LiteraturTaylor, M.P., Mankiw, N.G. (2014): Economics; Harvard Press

VSS (2006) SN 640 820: Kosten-Nutzen-Analysen im Strassenverkehr, VSS, Zürich.

Boardman, A.E., D.H. Greenberg, A.R. Vining und D.L. Weimer (2001) Cost – Benefit – Analysis: Concepts and Practise, Prentice-Hall, Upper Saddle River.

ecoplan and metron (2005) Kosten-Nutzen-Analysen im Strassenverkehr: Kommentar zu SN 640 820, UVEK, Bern.
Wahlfächer
Den Studierenden steht das gesamte Lehrangebot der ETH Zürich und der Universitäten Zürich zur individuellen Auswahl offen. Die Studeierenden haben selbst zu überprüfen, ob sie die Zulassungsvoraussetzungen zu einer Lehrveranstaltung erfüllen.
Empfohlene Wahlfächer des Studiengangs
Studierende, welche bereits im Rahmen des Bachelorstudiums oder als Auflagenfach für das Masterstudium die 851-0703-03 absolviert haben, dürfen diese im Rahmen des Masterstudiums nicht noch einmal belegen.
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
406-0242-AALAnalysis II Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-7 KP15RM. Akka Ginosar
KurzbeschreibungMathematical tools of an engineer
LernzielMathematics as a tool to solve engineering problems, mathematical formulation of problems in science and engineering. Basic mathematical knowledge of an engineers.
InhaltMulti variable calculus: gradient, directional derivative, chain rule, Taylor expansion, Lagrange multipliers. Multiple integrals: coordinate transformations, path integrals, integrals over surfaces, divergence theorem, applications in physics. Ordinary differential equations.
LiteraturTextbooks in English:
- J. Stewart: Multivariable Calculus, Thomson Brooks/Cole
- V. I. Smirnov: A course of higher mathematics. Vol. II. Advanced calculus
- W. L. Briggs, L. Cochran: Calculus: Early Transcendentals: International Edition, Pearson Education

- M. Akveld, R. Sperb, Analysis II, vdf
- L. Papula: Mathematik für Ingenieure 2, Vieweg Verlag
Rechnergestützte Wissenschaften Bachelor Information
Obligatorische Fächer des Basisjahres
Basisprüfungsblock 2
NummerTitelTypECTSUmfangDozierende
401-0231-10LAnalysis IO8 KP4V + 3UT. H. Willwacher
KurzbeschreibungReelle und komplexe Zahlen, Vektoren, Grenzwerte, Folgen, Reihen, Potenzreihen, stetige Abbildungen, Differential- und Integralrechnung einer Variablen, Einführung in gewöhnliche Differentialgleichungen
LernzielEinfuehrung in die Grundlagen der Analysis
SkriptKonrad Koenigsberger, Analysis I.
Christian Blatter: Ingenieur-Analysis (Kapitel 1-3)
Grundlagenfächer
Block G1
NummerTitelTypECTSUmfangDozierende
401-0353-00LAnalysis III Information O4 KP2V + 1UA. Figalli
KurzbeschreibungIn dieser Lehrveranstaltung werden Probleme der angewandten Analysis behandelt, speziell ausgerichtet auf die Bedürfnisse der Elektrotechniker. Dazu gehört vor allem das Studium der einfachsten Fälle der drei Grundtypen von partiellen Differentialgleichungen zweiten Grades: Laplace-Gleichung, Wärmeleitungsgleichung und Wellengleichung.
Lernziel
Inhalt1.) Klassifizierung von PDE's
- linear, quasilinear, nicht-linear
- elliptisch, parabolisch, hyperbolisch

2.) Quasilineare PDE
- Methode der Charakteristiken (Beispiele)

3.) Elliptische PDE
- Bsp: Laplace-Gleichung
- Harmonische Funktionen, Maximumsprinzip, Mittelwerts-Formel.
- Methode der Variablenseparation.

4.) Parabolische PDE
- Bsp: Wärmeleitungsgleichung
- Bsp: Inverse Wärmeleitungsgleichung
- Methode der Variablenseparation

5.) Hyperbolische PDE
- Bsp: Wellengleichung
- Formel von d'Alembert in (1+1)-Dimensionen
- Methode der Variablenseparation

6.) Green'sche Funktionen
- Rechnen mit der Dirac-Deltafunktion
- Idee der Green'schen Funktionen (Beispiele)

7.) Ausblick auf numerische Methoden
- 5-Punkt-Diskretisierung des Laplace-Operators (Beispiele)
LiteraturY. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005)

Zusätzliche Literatur:
Erwin Kreyszig, "Advanced Engineering Mathematics", John Wiley & Sons, Kap. 8, 11, 16 (sehr gutes Buch, als Referenz zu benutzen)
Norbert Hungerbühler, "Einführung in die partiellen Differentialgleichungen", vdf Hochschulverlag AG an der ETH Zürich.
G. Felder:Partielle Differenzialgleichungen.
https://people.math.ethz.ch/~felder/PDG/
Voraussetzungen / BesonderesVoraussetzungen: Analysis I und II, Fourier Reihen (Komplexe Analysis)
Vertiefungsgebiete
Computational Finance
NummerTitelTypECTSUmfangDozierende
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UA. Jentzen
KurzbeschreibungCourse on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.
LernzielThe aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.
InhaltGeneration of random numbers
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
SkriptLecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).
LiteraturP. Glassermann:
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.

P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
Voraussetzungen / BesonderesPrerequisites:

Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.

a) mandatory courses:
Elementary Probability,
Probability Theory I.

b) recommended courses:
Stochastic Processes.

Start of lectures: Wednesday, September 20, 2017

Date of the End-of-Semester examination: Wednesday, December 20, 2017, 13:00-15:00; students must arrive before 12:30 at ETH HG E 19.
Room for the End-of-Semester examination: ETH HG E 19.

Exam inspection: Monday, March 5, 2018,
13:00-14:00 at HG D 5.1
Please bring your legi.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
Rechnergestützte Wissenschaften Master Information
Vertiefungsgebiete
Computational Finance
NummerTitelTypECTSUmfangDozierende
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UA. Jentzen
KurzbeschreibungCourse on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.
LernzielThe aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.
InhaltGeneration of random numbers
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
SkriptLecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).
LiteraturP. Glassermann:
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.

P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
Voraussetzungen / BesonderesPrerequisites:

Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.

a) mandatory courses:
Elementary Probability,
Probability Theory I.

b) recommended courses:
Stochastic Processes.

Start of lectures: Wednesday, September 20, 2017

Date of the End-of-Semester examination: Wednesday, December 20, 2017, 13:00-15:00; students must arrive before 12:30 at ETH HG E 19.
Room for the End-of-Semester examination: ETH HG E 19.

Exam inspection: Monday, March 5, 2018,
13:00-14:00 at HG D 5.1
Please bring your legi.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
406-0353-AALAnalysis III Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RF. Da Lio
KurzbeschreibungEinführung in die partiellen Differentialgleichungen. Klassifizieren und Lösen von in der Praxis wichtigen Differentialgleichungen. Es werden elliptische, parabolische und hyperbolische Differentialgleichungen behandelt. Folgende mathematischen Techniken werden vorgestellt: Laplacetransformation, Fourierreihen, Separation der Variablen, Methode der Charakteristiken.
LernzielMathematische Behandlung naturwissenschaftlicher Probleme lernen. Verstehen der Eigenschaften der verschiedenen Typen von partiellen Differentialgleichungen.
InhaltLaplace Transforms:
- Laplace Transform, Inverse Laplace Transform, Linearity, s-Shifting
- Transforms of Derivatives and Integrals, ODEs
- Unit Step Function, t-Shifting
- Short Impulses, Dirac's Delta Function, Partial Fractions
- Convolution, Integral Equations
- Differentiation and Integration of Transforms

Fourier Series, Integrals and Transforms:
- Fourier Series
- Functions of Any Period p=2L
- Even and Odd Functions, Half-Range Expansions
- Forced Oscillations
- Approximation by Trigonometric Polynomials
- Fourier Integral
- Fourier Cosine and Sine Transform

Partial Differential Equations:
- Basic Concepts
- Modeling: Vibrating String, Wave Equation
- Solution by separation of variables; use of Fourier series
- D'Alembert Solution of Wave Equation, Characteristics
- Heat Equation: Solution by Fourier Series
- Heat Equation: Solutions by Fourier Integrals and Transforms
- Modeling Membrane: Two Dimensional Wave Equation
- Laplacian in Polar Coordinates: Circular Membrane, Fourier-Bessel Series
- Solution of PDEs by Laplace Transform
LiteraturE. Kreyszig, Advanced Engineering Mathematics, John Wiley & Sons, 10. Auflage, 2011

C. R. Wylie & L. Barrett, Advanced Engineering Mathematics, McGraw-Hill, 6th ed.
Stanley J. Farlow, Partial Differential Equations for Scientists and Engineers, (Dover Books on Mathematics).

G. Felder, Partielle Differenzialgleichungen für Ingenieurinnen und Ingenieure, hypertextuelle Notizen zur Vorlesung Analysis III im WS 2002/2003.

Y. Pinchover, J. Rubinstein, An Introduction to Partial Differential Equations, Cambridge University Press, 2005

For reference/complement of the Analysis I/II courses:

Christian Blatter: Ingenieur-Analysis (Download PDF)
Voraussetzungen / BesonderesWeitere Informationen unter:
http://www.math.ethz.ch/education/bachelor/lectures/hs2013/other/analysis3_itet
Robotics, Systems and Control Master Information
Kernfächer
NummerTitelTypECTSUmfangDozierende
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
227-0526-00LPower System Analysis Information W6 KP4GG. Hug
KurzbeschreibungZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
LernzielZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge und die Anwendung von Analysemethoden in stationären und dynamischen Zuständen des elektrischen Netzes.
InhaltDer Kurs beinhaltet die Herleitung von stationären und dynamischen Modellen des elektrischen Netzwerks, deren mathematische Darstellungen und spezielle Charakteristiken sowie Lösungsmethoden für die Behandlung von grossen linearen und nichtlinearen Gleichungssystemen im Zusammenhang mit dem elektrischen Netz. Ansätze wie der Netwon-Raphson Algorithmus angewendet auf die Lastflussgleichungen, Superpositions Prinzip für Kurzschlussberechnung, Methoden für Stabilitätsanalysen und Lastflussberechnungsmethoden für das Verteilnetz werden präsentiert.
SkriptVorlesungsskript.
Science, Technology, and Policy Master Information
Kernfächer
NummerTitelTypECTSUmfangDozierende
860-0002-00LQuantitative Policy Analysis and ModelingO6 KP4GA. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet
KurzbeschreibungThe lectures will introduce students to the principles of quantitative policy analysis, namely the methods to predict and evaluate the social, economic, and environmental effects of alternative strategies to achieve public objectives. A series of graded assignments will give students an opportunity for students to apply those methods to a set of case studies
LernzielThe objectives of this course are to develop the following key skills necessary for policy analysts:
- Identifying the critical quantitative factors that are of importance to policy makers in a range of decision-making situations.
- Developing conceptual models of the types of processes and relationships governing these quantitative factors, including stock-flow dynamics, feedback loops, optimization, sources and effects of uncertainty, and agent coordination problems.
- Develop and program numerical models to simulate the processes and relationships, in order to identify policy problems and the effects of policy interventions.
- Communicate the findings from these simulations and associated analysis in a manner that makes transparent their theoretical foundation, the level and sources of uncertainty, and ultimately their applicability to the policy problem.
The course will proceed through a series of policy analysis and modeling exercises, involving real-world or hypothetical problems. The specific examples around which work will be done will concern the environment, energy, health, and natural hazards management.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
101-0439-00LIntroduction to Economic Analysis - A Case Study Approach with Cost Benefit Analysis in Transport
Remark:
Former Title "Introduction to Economic Policy - A Case Study Approach with Cost Benefit Analysis in Transport".
W6 KP4GK. W. Axhausen, R. Schubert
KurzbeschreibungDie Vorlesung stellt einige grundlegende ökonomische Prinzipien sowie die Verfahren der Kosten-Nutzen-Analyse vor; sie führt auch in Methoden zur Ermittlung von Bewertungsgrössen ein
LernzielSichere Kenntnis mikro- und makroökonomischer Grundlagen. Erarbeitung und Übung von Verfahren der Bewertung von Massnahmen und infrastrukturellen Ausbauten
InhaltMikro-und makroökonomische Grundlagen; Kosten - Nutzen - Analyse; Nutzwertanalyse; Europäische Richtlinien; Stated response Verfahren; Reisekostenansatz et al.; Bewertung von Reisezeitveränderungen; Bewertung der Verkehrssicherheit
Skriptmoodle Plattform für die ökonomischen Grundlagen; Umdrucke
LiteraturTaylor, M.P., Mankiw, N.G. (2014): Economics; Harvard Press

VSS (2006) SN 640 820: Kosten-Nutzen-Analysen im Strassenverkehr, VSS, Zürich.

Boardman, A.E., D.H. Greenberg, A.R. Vining und D.L. Weimer (2001) Cost – Benefit – Analysis: Concepts and Practise, Prentice-Hall, Upper Saddle River.

ecoplan and metron (2005) Kosten-Nutzen-Analysen im Strassenverkehr: Kommentar zu SN 640 820, UVEK, Bern.
Sport Lehrdiplom Information
Detaillierte Informationen zum Studiengang auf: www.didaktischeausbildung.ethz.ch
Auflagen Sportwissenschaft
NummerTitelTypECTSUmfangDozierende
376-2019-00LAngewandte Bewegungsanalyse Information W2 KP2GR. Scharpf, S. Lorenzetti
KurzbeschreibungAnhand von praktischen Beispielen aus Sport, Alltag und Therapie werden verschiedene Methoden der Bewegungsanalyse angewendet und verglichen.
LernzielDie Studierenden können menschliche Bewegungen mithilfe verschiedener Methoden der Bewegungsanalyse gezielt beurteilen.
InhaltIm Verlauf des Studiums lernen Studierende verschiedene Methoden der Bewegungsanalyse kennen: Funktionale, morphologische, klinische, mechanische, systemdynamische, usw.
Diese werden anhand von konkreten Beispielen angewendet und gegenübergestellt. Basis bilden Bewegungen aus Sport, Alltag und Therapie wie Unihockey, Geräteturnen/ Akrobatik, Badminton, Gehen/ Laufen, Krafttraining.
In einer ersten Phase der Vorlesung werden die Ansätze im Plenum vorgestellt und praktisch umgesetzt. In einer zweiten werden individuelle Projekte in kleinen Teams ausgearbeitet, vorgestellt und bewertet.
SkriptAllfällige Unterlagen werden auf moodle zur Verfügung gestellt.
Statistik Master Information
Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.
Kernfächer
In der Regel werden die Kernfächer in jedem Themenbereich sowohl in einer mathematisch ausgerichteten als auch in einer anwendungsorientierten Art angeboten. Pro Themenbereich wird jeweils nur eine dieser beiden Arten für das Master-Diplom angerechnet.
Varianzanalyse und Versuchsplanung
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
Zeitreihen und stochastische Prozesse
NummerTitelTypECTSUmfangDozierende
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations.
Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations,
spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
Vertiefungs- und Wahlfächer
Statistische und mathematische Fächer
NummerTitelTypECTSUmfangDozierende
401-6217-00LUsing R for Data Analysis and Graphics (Part II) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions.
Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known.
LernzielThe students will be able to use the software R efficiently for data analysis.
InhaltThe course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part II of the course builds on part I and covers the following additional topics:
- Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects;
- More on R functions;
- Applying functions to elements of vectors, matrices and lists;
- Object oriented programming with R: classes and methods;
- Tayloring R: options
- Extending basic R: packages

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesBasic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course.

The course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
401-6282-00LStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: STA426

Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html
W5 KP3GH. Rehrauer, M. Robinson
KurzbeschreibungA range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments.
Lernziel-Understand the fundamental "scientific process" in the field of Statistical Bioinformatics
-Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave)
-Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data
-Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data
-Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project
-Gain the ability to critical assess the statistical bioinformatics literature
-Write a coherent summary of a bioinformatics problem and its solution in statistical terms
InhaltLectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification
SkriptLecture notes, published manuscripts
Voraussetzungen / BesonderesPrerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics

Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data
Statistische und mathematische Fächer: nicht wählbar für Kreditpunkte
NummerTitelTypECTSUmfangDozierende
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information E-1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
Umweltingenieurwissenschaften Bachelor Information
1. Semester
Basisprüfung (1. Sem.)
NummerTitelTypECTSUmfangDozierende
401-0241-00LAnalysis I Information O7 KP5V + 2UM. Akka Ginosar
KurzbeschreibungMathematische Hilfsmittel des Ingenieurs
LernzielMathematik als Hilfsmittel zur Lösung von Ingenieurproblemen:
Verständnis für mathematische Formulierung von technischen und naturwissenschaftlichen Problemen.
Erarbeitung des mathematischen Grundwissens für einen Ingenieur.
InhaltKomplexe Zahlen.
Differentialrechnung und Integralrechnung für Funktionen einer Variablen mit Anwendungen.
Einfache mathematische Modelle in den Naturwissenschaften.
SkriptDie Vorlesung folgt weitgehend

Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer; online verfügbar unter:
http://link.springer.com/book/10.1007/978-3-8348-2559-9/page/1
LiteraturNeben Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer sind auch die folgenden Bücher/Skripte empfehlenswert und decken den zu behandelnden Stoff ab:

Tilo Arens et al., "Mathematik", Springer; online verfügbar unter:
http://link.springer.com/book/10.1007/978-3-642-44919-2/page/1

Meike Akveld, "Analysis 1", vdf;
http://vdf.ch/index.php?route=product/product&product_id=1706

Urs Stammbach, "Analysis I/II" (erhältlich im ETH Store);
https://people.math.ethz.ch/~stammb/analysisskript.html
5. Semester
Wahlmodule
Wahlmodul Energie
NummerTitelTypECTSUmfangDozierende
227-1631-00LEnergy System Analysis Information W4 KP3GG. Hug, S. Hellweg, F. Noembrini, A. Schlüter
KurzbeschreibungThe course provides an introduction to the methods and tools for analysis of energy consumption, energy production and energy flows. Environmental aspects are included as well as economical considerations. Different sectors of the society are discussed, such as electric power, buildings, and transportation. Models for energy system analysis planning are introduced.
LernzielThe purpose of the course is to give the participants an overview of the methods and tools used for energy systems analysis and how to use these in simple practical examples.
InhaltThe course gives an introduction to methods and tools for analysis of energy consumption, energy production and energy flows. Both larger systems, e.g. countries, and smaller systems, e.g. industries, homes, vehicles, are studied. The tools and methods are applied to various problems during the exercises. Different conventions of energy statistics used are introduced.

The course provides also an introduction to energy systems models for developing scenarios of future energy consumption and production. Bottom-up and Top-Down approaches are addressed and their features and applications discussed.

The course contains the following parts:
Part I: Energy flows and energy statistics
Part II: Environmental impacts
Part III: Electric power systems
Part IV: Energy in buildings
Part V: Energy in transportation
Part VI: Energy systems models
SkriptHandouts
LiteraturExcerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8
Umweltingenieurwissenschaften Master Information
Master-Studium (Studienreglement 2016)
Vertiefungen
Vertiefung Siedlungswasserwirtschaft
Obligatorische Module
System Analysis in Urban Water Management
NummerTitelTypECTSUmfangDozierende
102-0227-00LSystems Analysis and Mathematical Modeling in Urban Water Management Information O6 KP4GE. Morgenroth, M. Maurer
KurzbeschreibungSystematic introduction of material balances, transport processes, kinetics, stoichiometry and conservation. Ideal reactors, residence time distribution, heterogeneous systems, dynamic response of reactors. Parameter identification, local sensitivity, error propagation, Monte Carlo simulation. Introduction to real time control (PID controllers). Extensive coding of examples in Berkeley Madonna.
LernzielThe goal of this course is to provide the students with an understanding and the tools to develop their own mathematical models, to plan experiments, to evaluate error propagation and to test simple process control strategies in the field of process engineering in urban water management.
InhaltThe course will provide a broad introduction into the fundamentals of modeling water treatment systems. The topics are:
- Introduction into modeling and simulation
- The material balance equations, transport processes, transformation processes (kinetics, stoichiometry, conservation)
- Ideal reactors
- Hydraulic residence time distribution and modeling of real reactors
- Dynamic behavior of reactor systems
- Systems analytical tools: Sensitivity, parameter identification, error propagation, Monte Carlo simulation
- Introduction to process control (PID controller, fuzzy control)
SkriptCopies of overheads will be made available.
LiteraturThere will be a required textbook that students need to purchase:
Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg
Voraussetzungen / BesonderesThis course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously.
Vertiefung Umwelttechnologien
Obligatorische Module
System Analysis in Urban Water Management
inweis: Studierende, welche WASTE und SysUMW belegen, müssen die 102-0337-00 Landfilling, Contaminated Sites and Radioactive Waste Repositories als Ersatz für 102-0217-00 Process Engineering Ia im Modul WASTE belegen, welche in beiden Modulen vorkommt.
NummerTitelTypECTSUmfangDozierende
102-0227-00LSystems Analysis and Mathematical Modeling in Urban Water Management Information O6 KP4GE. Morgenroth, M. Maurer
KurzbeschreibungSystematic introduction of material balances, transport processes, kinetics, stoichiometry and conservation. Ideal reactors, residence time distribution, heterogeneous systems, dynamic response of reactors. Parameter identification, local sensitivity, error propagation, Monte Carlo simulation. Introduction to real time control (PID controllers). Extensive coding of examples in Berkeley Madonna.
LernzielThe goal of this course is to provide the students with an understanding and the tools to develop their own mathematical models, to plan experiments, to evaluate error propagation and to test simple process control strategies in the field of process engineering in urban water management.
InhaltThe course will provide a broad introduction into the fundamentals of modeling water treatment systems. The topics are:
- Introduction into modeling and simulation
- The material balance equations, transport processes, transformation processes (kinetics, stoichiometry, conservation)
- Ideal reactors
- Hydraulic residence time distribution and modeling of real reactors
- Dynamic behavior of reactor systems
- Systems analytical tools: Sensitivity, parameter identification, error propagation, Monte Carlo simulation
- Introduction to process control (PID controller, fuzzy control)
SkriptCopies of overheads will be made available.
LiteraturThere will be a required textbook that students need to purchase:
Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg
Voraussetzungen / BesonderesThis course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously.
Wählbare Module
Für alle Vertiefungen
WM: System Analysis in Urban Water Management
Wählbares Modul für die Vertiefungen "Fluss- und Wasserbau", "Ressourcenmanagement" und "Wasserwirtschaft".

Hinweis: Studierende, welche WASTE und SysUMW belegen, müssen die 102-0337-00 Landfilling, Contaminated Sites and Radioactive Waste Repositories als Ersatz für 102-0217-00 Process Engineering Ia im Modul WASTE belegen, welche in beiden Modulen vorkommt.
NummerTitelTypECTSUmfangDozierende
102-0227-00LSystems Analysis and Mathematical Modeling in Urban Water Management Information W6 KP4GE. Morgenroth, M. Maurer
KurzbeschreibungSystematic introduction of material balances, transport processes, kinetics, stoichiometry and conservation. Ideal reactors, residence time distribution, heterogeneous systems, dynamic response of reactors. Parameter identification, local sensitivity, error propagation, Monte Carlo simulation. Introduction to real time control (PID controllers). Extensive coding of examples in Berkeley Madonna.
LernzielThe goal of this course is to provide the students with an understanding and the tools to develop their own mathematical models, to plan experiments, to evaluate error propagation and to test simple process control strategies in the field of process engineering in urban water management.
InhaltThe course will provide a broad introduction into the fundamentals of modeling water treatment systems. The topics are:
- Introduction into modeling and simulation
- The material balance equations, transport processes, transformation processes (kinetics, stoichiometry, conservation)
- Ideal reactors
- Hydraulic residence time distribution and modeling of real reactors
- Dynamic behavior of reactor systems
- Systems analytical tools: Sensitivity, parameter identification, error propagation, Monte Carlo simulation
- Introduction to process control (PID controller, fuzzy control)
SkriptCopies of overheads will be made available.
LiteraturThere will be a required textbook that students need to purchase:
Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg
Voraussetzungen / BesonderesThis course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously.
Master-Studium (Studienreglement 2006)
Vertiefungsfächer (Majors)
Vertiefung Siedlungswasserwirtschaft
NummerTitelTypECTSUmfangDozierende
102-0227-00LSystems Analysis and Mathematical Modeling in Urban Water Management Information O6 KP4GE. Morgenroth, M. Maurer
KurzbeschreibungSystematic introduction of material balances, transport processes, kinetics, stoichiometry and conservation. Ideal reactors, residence time distribution, heterogeneous systems, dynamic response of reactors. Parameter identification, local sensitivity, error propagation, Monte Carlo simulation. Introduction to real time control (PID controllers). Extensive coding of examples in Berkeley Madonna.
LernzielThe goal of this course is to provide the students with an understanding and the tools to develop their own mathematical models, to plan experiments, to evaluate error propagation and to test simple process control strategies in the field of process engineering in urban water management.
InhaltThe course will provide a broad introduction into the fundamentals of modeling water treatment systems. The topics are:
- Introduction into modeling and simulation
- The material balance equations, transport processes, transformation processes (kinetics, stoichiometry, conservation)
- Ideal reactors
- Hydraulic residence time distribution and modeling of real reactors
- Dynamic behavior of reactor systems
- Systems analytical tools: Sensitivity, parameter identification, error propagation, Monte Carlo simulation
- Introduction to process control (PID controller, fuzzy control)
SkriptCopies of overheads will be made available.
LiteraturThere will be a required textbook that students need to purchase:
Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg
Voraussetzungen / BesonderesThis course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously.
Fachspezifische Wahlfächer (Minors)
NummerTitelTypECTSUmfangDozierende
102-0227-00LSystems Analysis and Mathematical Modeling in Urban Water Management Information W6 KP4GE. Morgenroth, M. Maurer
KurzbeschreibungSystematic introduction of material balances, transport processes, kinetics, stoichiometry and conservation. Ideal reactors, residence time distribution, heterogeneous systems, dynamic response of reactors. Parameter identification, local sensitivity, error propagation, Monte Carlo simulation. Introduction to real time control (PID controllers). Extensive coding of examples in Berkeley Madonna.
LernzielThe goal of this course is to provide the students with an understanding and the tools to develop their own mathematical models, to plan experiments, to evaluate error propagation and to test simple process control strategies in the field of process engineering in urban water management.
InhaltThe course will provide a broad introduction into the fundamentals of modeling water treatment systems. The topics are:
- Introduction into modeling and simulation
- The material balance equations, transport processes, transformation processes (kinetics, stoichiometry, conservation)
- Ideal reactors
- Hydraulic residence time distribution and modeling of real reactors
- Dynamic behavior of reactor systems
- Systems analytical tools: Sensitivity, parameter identification, error propagation, Monte Carlo simulation
- Introduction to process control (PID controller, fuzzy control)
SkriptCopies of overheads will be made available.
LiteraturThere will be a required textbook that students need to purchase:
Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg
Voraussetzungen / BesonderesThis course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously.
101-0187-00LStructural Reliability and Risk Analysis Information W3 KP2GS. Marelli
KurzbeschreibungStructural reliability aims at quantifying the probability of failure of systems due to uncertainties in their design, manufacturing and environmental conditions. Risk analysis combines this information with the consequences of failure in view of optimal decision making. The course presents the underlying probabilistic modelling and computational methods for reliability and risk assessment.
LernzielThe goal of this course is to provide the students with a thorough understanding of the key concepts behind structural reliability and risk analysis. After this course the students will have refreshed their knowledge of probability theory and statistics to model uncertainties in view of engineering applications. They will be able to analyze the reliability of a structure and to use risk assessment methods for decision making under uncertain conditions. They will be aware of the state-of-the-art computational methods and software in this field.
InhaltEngineers are confronted every day to decision making under limited amount of information and uncertain conditions. When designing new structures and systems, the design codes such as SIA or Euro- codes usually provide a framework that guarantees safety and reliability. However the level of safety is not quantified explicitly, which does not allow the analyst to properly choose between design variants and evaluate a total cost in case of failure. In contrast, the framework of risk analysis allows one to incorporate the uncertainty in decision making.

The first part of the course is a reminder on probability theory that is used as a main tool for reliability and risk analysis. Classical concepts such as random variables and vectors, dependence and correlation are recalled. Basic statistical inference methods used for building a probabilistic model from the available data, e.g. the maximum likelihood method, are presented.

The second part is related to structural reliability analysis, i.e. methods that allow one to compute probabilities of failure of a given system with respect to prescribed criteria. The framework of reliability analysis is first set up. Reliability indices are introduced together with the first order-second moment method (FOSM) and the first order reliability method (FORM). Methods based on Monte Carlo simulation are then reviewed and illustrated through various examples. By-products of reliability analysis such as sensitivity measures and partial safety coefficients are derived and their links to structural design codes is shown. The reliability of structural systems is also introduced as well as the methods used to reassess existing structures based on new information.

The third part of the course addresses risk assessment methods. Techniques for the identification of hazard scenarios and their representation by fault trees and event trees are described. Risk is defined with respect to the concept of expected utility in the framework of decision making. Elements of Bayesian decision making, i.e. pre-, post and pre-post risk assessment methods are presented.

The course also includes a tutorial using the UQLab software dedicated to real world structural reliability analysis.
SkriptSlides of the lectures are available online every week. A printed version of the full set of slides is proposed to the students at the beginning of the semester.
LiteraturAng, A. and Tang, W.H, Probability Concepts in Engineering - Emphasis on Applications to Civil and Environmental Engineering, 2nd Edition, John Wiley & Sons, 2007.

S. Marelli, R. Schöbi, B. Sudret, UQLab user manual - Structural reliability (rare events estimation), Report UQLab-V0.92-107.
Voraussetzungen / BesonderesBasic course on probability theory and statistics
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
102-0324-AALEcological Systems Analysis Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-6 KP4RS. Hellweg
KurzbeschreibungMethodological basics and application of various environmental assessment tools.
LernzielStudents learn about environmental assessment tools, such as material flow analysis, risk assessment, and life cycle assessment. They can identify and apply the appropriate tool in a given situation. Also, they are able to critically assess existing studies.
Inhalt- Methodological basics of material flow analysis, risk assessment and life cycle assessment
- Application of these methods to case studies
SkriptNo script, but literature available on homepage.
LiteraturLiterature available on
Link
Voraussetzungen / BesonderesNone
Umweltnaturwissenschaften Bachelor Information
Bachelor-Studium (Studienreglement 2016)
Grundlagenfächer I
Basisprüfung
NummerTitelTypECTSUmfangDozierende
401-0251-00LMathematik I: Analysis I und Lineare AlgebraO6 KP4V + 2UL. Halbeisen
KurzbeschreibungDiese Vorlesung behandelt mathematische Konzepte und Methoden, die zum Modellieren, Lösen und Diskutieren wissenschaftlicher Probleme nötig sind - speziell durch gewöhnliche Differentialgleichungen.
LernzielMathematik ist von immer grösserer Bedeutung in den Natur- und Ingenieurwissenschaften. Grund dafür ist das folgende Konzept zur Lösung konkreter Probleme: Der entsprechende Ausschnitt der Wirklichkeit wird in der Sprache der Mathematik modelliert; im mathematischen Modell wird das Problem - oft unter Anwendung von äusserst effizienter Software - gelöst und das Resultat in die Realität zurück übersetzt.

Ziel der Vorlesungen Mathematik I und II ist es, die einschlägigen mathematischen Grundlagen bereit zu stellen. Differentialgleichungen sind das weitaus wichtigste Hilfsmittel im Prozess des Modellierens und stehen deshalb im Zentrum beider Vorlesungen.
Inhalt1. Differential- und Integralrechnung:
Wiederholung der Ableitung, Linearisierung, Taylor-Polynome, Extremwerte, Stammfunktion, Hauptsatz der Differential- und Integralrechnung, Integrationsmethoden, uneigentliche Integrale.

2. Lineare Algebra und Komplexe Zahlen:
lineare Gleichungssysteme, Gauss-Verfahren, Matrizen, Determinanten, Eigenwerte und Eigenvektoren, Darstellungsformen der komplexe Zahlen, Potenzieren, Radizieren, Fundamentalsatz der Algebra.

3. Gewöhnliche Differentialgleichungen:
Separierbare Differentialgleichungen (DGL), Integration durch Substitution, Lineare DGL erster und zweiter Ordnung, homogene Systeme linearer DGL mit konstanten Koeffizienten, Einführung in die dynamischen Systeme in der Ebene.
Literatur- Thomas, G. B., Weir, M. D. und Hass, J.: Analysis 1, Lehr- und Übungsbuch (Pearson).
- Gramlich, G.: Lineare Algebra, eine Einführung (Hanser).
- Papula, L.: Mathematik für Ingenieure und Naturwissenschaftler, Bd. 1 und 2 (Vieweg+Teubner).
Voraussetzungen / BesonderesVoraussetzungen: Vertrautheit mit den Grundlagen der Analysis, insbesondere mit dem Funktions- und Ableitungsbegriff.

Mathe-Lab (Präsenzstunden):
Mo 12-14, Di 17-19, Mi 17-19, stets im Raum HG E 41.
Grundlagenfächer II
Prüfungsblöcke
Prüfungsblock 2
NummerTitelTypECTSUmfangDozierende
701-0071-00LMathematik III: SystemanalyseO4 KP2V + 1UN. Gruber, M. Vogt
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
Naturwissenschaftliche und technische Wahlfächer
Methoden der statistischen Datenanalyse
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
401-6217-00LUsing R for Data Analysis and Graphics (Part II) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions.
Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known.
LernzielThe students will be able to use the software R efficiently for data analysis.
InhaltThe course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part II of the course builds on part I and covers the following additional topics:
- Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects;
- More on R functions;
- Applying functions to elements of vectors, matrices and lists;
- Object oriented programming with R: classes and methods;
- Tayloring R: options
- Extending basic R: packages

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesBasic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course.

The course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
Systemvertiefung
Mensch-Umwelt Systeme
NummerTitelTypECTSUmfangDozierende
701-0651-00LKoevolution zwischen Gesellschaft und Umwelt: Analyse und EinflussnahmeW3 KP2VJ. Minsch
KurzbeschreibungGrundlagen einer ökonomisch-sozialwissenschaftlichen Analyse der gesellschaftlichen Entwicklung. Leitorientierung: umfassend verstandene Nachhaltige Entwicklung. Outcome: innovative Zukunftsstrategien für Wirtschaft, Politik und Zivilgesellschaft. Wiss. Zugang: Ökologische Ökonomie, Entwicklungstheorie, Institutionen- und Innovationstheorie, Theorie liberaler Wirtschafts- und Gesellschaftspolitik.
LernzielAllgemeine Zielsetzung:
Einführung in die Grundlagen einer handlungsorientierten, ökonomisch-sozialwissenschaftlichen Analyse zentraler gesellschaftlicher Mechanismen vor dem Hintergrund (1) der Leitidee Nachhaltiger Entwicklung und (2) der Tatsache einer "Globalen Grossen Transformation" (wirtschaftlich, politisch, ökologisch und technisch).

Methodisches Wissen:
Die Studierenden werden vertraut gemacht mit ausgewählten Diskursen und Analyseansätzen aus den Bereichen Ökologische Ökonomie, Theorie der gesellschaftlichen Entwicklung, Institutionentheorie, Innovationstheorie, Welthandelslehre, Theorie einer menschenrechtsbasierten, liberalen Wirtschafts- und Gesellschaftspolitik.

Vermittelte Fähigkeiten:
1) Zielwissen: Die Studierenden werden mit Idee und Deutungsspektrum des Begriffs „Nachhaltige Entwicklung“ vertraut gemacht und in die Lage versetzt, sich kreativ in den aktuellen Nachhaltigkeitsdiskurs einzubringen. Hierzu gehört auch die Fähigkeit, die nachhaltigkeitsrelevanten Fragen im eigenen Fachgebiet zu identifizieren und zu erarbeiten. Motto: "Das Richtige tun, nicht das Überholte nachbessern!"

2) Analysewissen: Die Veranstaltung legt Grundlagen, die die Studierenden als Akteure in Wirtschaft, Politik und Gesellschaft in die Lage versetzen, reflektiert die tieferen Ursachen der heutigen Nichtnachhaltigkeit zu verstehen und zu erkennen, dass wir mitten in einer Globalen Grossen Transformation stecken - mit ihren Chancen und Gefahren.

3) Transformationswissen: Die Veranstaltung öffnet den Blick auf notwendige innovative Lösungsstrategien in den Bereichen Wirtschaft / Unternehmen, Politik, Zivilgesellshaft - jenseits von kurzsichtigem Pragmatismus und Symptombekämpfung.
InhaltKurzes Nachhaltigkeits-Update:
Ursprünge der Leitidee Nachhaltige Entwicklung, normative Grundlagen, Konzepte. Was bleibt gültig nach 25 Jahren Nachhaltigkeitsdiskurs?

Entwicklung als Freiheit:
Woran hängt es, dass Gesellschaften sich entwickeln und neue Wege beschreiten oder aber scheitern? Grundlagen einer Theorie der gesellschaftlichen Entwicklung, auf der Basis der Werke von Amartya Sen (2002), Daron Acemoglu / James A. Robinson (2013) und Jared Diamond (2005), unter Berücksichtigung u.a. von K.R. Popper, F.A.v. Hayek, R. Dahrendorf.

Konzeptionelle Grundlagen der Marktwirtschaft:
Die Ideen der Klassiker Walter Eucken und Ludwig Erhard. Was wurde daraus in den letzten 50 Jahren? Wie kann die Marktwirtschaft zukunftsfähig gemacht werden? Was wäre eine "zivilisierte Marktwirtschaft" (Peter Ulrich)?

Das "Neomerkantilismus-Syndrom":
Wie eine Politik der billigen Zentralressourcen, des billigen Geldes und der asymmetrischen Globalisierung uns in den letzten 50 Jahren Wohlstand brachte - und an die ökologischen und gesellschaftlichen Grenzen führte.

Wachstumskritik 2016:
Neuere Positionen zur Wachstumsfrage: "Die Wachstumsspirale: Geld, Energie und Imagination in der Dynamik des Marktprozesses" (H.C. Binswanger), "Prosperität ohne Wachstum?" (T. Jackson), "Intelligent wachsten!" (R. Fücks)

"Das Internet der Dinge":
Zu einem neuen Trend, der das Zeug hat, das Wirtschaftsleben grundlegend zu verändern - Tatsachen, Reflexionen, Perspektiven

Suffizienz:
Perspektiven einer resourcenleichten Gesellschaft

"Unternehmung 2020":
Umweltmanagement und CSR in Ehren, aber es braucht mehr: Zur Unternehmens-DNA der Zukunft (P. Sukhdev)

Zur Anatomie der Finanz- und Verschuldungskrise:
Ein aktueller Zwischenbericht zu einer fast unendlichen Geschichte - mit Bezügen zur ökologischen und sozialen Frage

Globalisierung:
Tatsachen und Reflexionen zu einem globalen Megatrend. Grundlagen einer fairen Globalisierung. Wie lässt sich ein Komplexphänomen wie die Globalisierung eigentlich gestalten?

"Fluch der Ressourcen":
Ressourcenreichtum kann arm machen. Zu den Zusammenhängen zwischen Ressourcenvorkommen, Ressourcenzugang, Demokratie und wirtschaftlicher Entwicklung, dargestellt und diskutiert anhand ausgewählter Länderbeispiele. Fluch der Ressourcen auch in der Schweiz?

Auf die Institutionen kommt es an!
Institutionentheoretische Grundlagen zur Gestaltung gesellschaftlicher Mechanismen. Überblick und Reflexion über das "Universum" konkreter institutioneller Innovationen für eine Nachhaltige Entwicklung in Zeiten grundlegender Transformationen. Im Grunde müssen wir Demokratie und Marktwirtschaft neu erfinden - oder: Lasst uns an den "Federalist Papers" weiterschreiben!

Prolog zur Synthese:
Die Erste Industrielle Revolution. Welches waren die wichtigsten Wirkungszusammenhänge und welches war das zugrunde liegende "Energie-Kommunikations-Mobilitäts-System"? Was ist heute ähnlich, was anders? Lehren

Synthese:
Die Grosse Globale Transformation ist Realität - man muss sie nur erkennen! Umrisse des sich abzeichnenden neuen "Energie-Kommunikations-Mobilitäts-Systems". Vor diesem Hintergrund: Zusammenführung der Inhalte der LV, Perspektiven & weiterführende Fragen
SkriptSkriptum und Zusatzunterlagen werden in der Lehrveranstaltung abgegeben
LiteraturEine erste Auswahl:
- Daron Acemoglu / James A. Robinson (2013): Warum Nationen scheitern. Die Ursprünge von Macht, Wohlstand und Armut, Frankfurt am Main
- Hans Christoph Binswanger (2006): Die Wachstumsspirale. Geld, Energie und Imagination in der Dynamik des Marksprozesses, Marburg
- Ralf Dahrendorf ( 2003): Auf der Suche nach einer neuen Ordnung, München
- Jared Diamond (2006): Kollaps - Warum Gesellschaften überleben oder untergehen. Frankfurt am Main (Amerikanische Originalausgabe: Collapse: How Societies Choose to Fail or Succeed, New York 2005)
- Ralf Fücks (2013): Intelligent wachsten, Die grüne Revolution, München
- Friedrich A. von Hayek (1991): Die Verfassung der Freiheit, 3. Auflage, Tübingen
- Friedrich A. von Hayek (1972): Theorie komplexer Phänomene, Tübingen
- Tim Jackson (2009): Prosperity without Growth. Economics for a Finite Planet, London
- Jürg Minsch / Peter H. Feindt / Hans. P. Meister / Uwe Schneidewind / Tobias Schulz (1998): Institutionelle Reformen für eine Politik der Nachhaltigkeit, Berlin / Heidelberg / New York
- J. Minsch / A. Eberle / B. Meier / U. Schneidewind (1996). Mut zum ökologischen Umbau. Innovationsstrategien für Unternehmen, Politik und Akteurnetze, Birkhäuser, Basel / Boston / Berlin
- Elinor Ostrom (1990): Die Verfassung der Allmende, Tübingen (Amerikanische Originalausgabe: Governing the Commons, Cambridge University Press, Cambridge / New York / Melbourne 1990)
- oekom e.V., Hrsg. (2013): Baustelle Zukunft. Die Grosse Trasformation von Wirtschaft und Gesellschaft, oekom Verlag, München
- Karl Polanyi (1978): The Great Transformation. Politische und ökonomische Ursprünge von Gesellschaften und Wirtschaftssystemen, suhrkamp Verlag, Frankfurt (Originalausgabe (1944): The Great Transformation)
- Karl. R. Popper (1980): Die offene Gesellschaft und ihre Feinde, Bde. I und II, 6. Auflage, Tübingen
- Jeremy Rifkin (2014): Die Null Grenzkosten Gesellschaft. Das Internet der Dinge, Kollaboratives Gemeingut und der Rückzug des Kapitalismus, Campus, Frankfurt am Main
- Uwe Schneidewind / Angelika Zahrnt (2013): Damit gutes Leben einfacher wird. Perspektiven einer Suffizienzpolitik, München
- Pavan Sukhdev (2013): Corporation 2020. Warum wir Wirtschaft neu denken müssen, München
- Tomas Sedlacek (2012): Die Ökonomie von Gut und Böse, München
- Amartya Sen (2002): Ökonomie für den Menschen. Wege zur Gerechtigkeit und Solidarität in der Marktwirtschaft, München (Amerikanische Originalausgabe: Development as Freedom, New York 1999)
-Daniel Spreng /Thomas Flüeler /David Goldblatt /Jürg Minsch (2012): Tackling Long Term Global Energy Problems: The Contribution of Social Science, Dortrecht / Heidelberg / New York
- Joseph Stiglitz (2006): Die Chancen der Globalisierung, München (Amerikanische Originalausgabe: Making Globalization Work, New York 2006)
- Peter Ulrich (2005): Zivilisierte Marktwirtschaft, 2. Aufl., Freiburg
- WBGU Wissenschaftlicher Beirat der Bundesregierung Globale Umweltveränderungen (2011): Welt im Wandel. Gesellschaftsvertrag für eine Grosse Transformation, Zusammenfassung für Entscheidungsträger, WBGU, Berlin, http://www.wbgu.de

Weitere Angaben in der Vorlesung
Voraussetzungen / BesonderesErwartet wird die Bereitschaft zur individuellen vertiefenden Auseinandersetzung mit der behandelten Thematik und die aktive Teilnahme an den Diskussionen
Bachelor-Studium (Studienreglement 2011)
Grundlagenfächer II
Prüfungsblöcke
Prüfungsblock 2
NummerTitelTypECTSUmfangDozierende
701-0071-00LMathematik III: SystemanalyseO4 KP2V + 1UN. Gruber, M. Vogt
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
Naturwissenschaftliche und technische Wahlfächer
Naturwissenschaftliche Module
Methoden der statistischen Datenanalyse
NummerTitelTypECTSUmfangDozierende
401-0625-01LApplied Analysis of Variance and Experimental Design Information W5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-6215-00LUsing R for Data Analysis and Graphics (Part I) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.
LernzielThe students will be able to use the software R for simple data analysis.
InhaltThe course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part I of the course covers the following topics:
- What is R?
- R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics;
- Types of data: numeric, character, logical and categorical data, missing values;
- Simple (statistical) functions: summary, mean, var, etc., simple statistical tests;
- Writing simple functions;
- Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots.

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org

Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I.
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesThe course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
401-6217-00LUsing R for Data Analysis and Graphics (Part II) Information W1.5 KP1GA. Drewek, M. Mächler
KurzbeschreibungThe course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions.
Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known.
LernzielThe students will be able to use the software R efficiently for data analysis.
InhaltThe course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R.

Part II of the course builds on part I and covers the following additional topics:
- Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects;
- More on R functions;
- Applying functions to elements of vectors, matrices and lists;
- Object oriented programming with R: classes and methods;
- Tayloring R: options
- Extending basic R: packages

The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org
SkriptAn Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf
Voraussetzungen / BesonderesBasic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course.

The course resources will be provided via the Moodle web learning platform
Please login (with your ETH (or other University) username+password) at
https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145
Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration.
Systemvertiefung
Mensch-Umwelt Systeme
NummerTitelTypECTSUmfangDozierende
701-0651-00LKoevolution zwischen Gesellschaft und Umwelt: Analyse und EinflussnahmeW3 KP2VJ. Minsch
KurzbeschreibungGrundlagen einer ökonomisch-sozialwissenschaftlichen Analyse der gesellschaftlichen Entwicklung. Leitorientierung: umfassend verstandene Nachhaltige Entwicklung. Outcome: innovative Zukunftsstrategien für Wirtschaft, Politik und Zivilgesellschaft. Wiss. Zugang: Ökologische Ökonomie, Entwicklungstheorie, Institutionen- und Innovationstheorie, Theorie liberaler Wirtschafts- und Gesellschaftspolitik.
LernzielAllgemeine Zielsetzung:
Einführung in die Grundlagen einer handlungsorientierten, ökonomisch-sozialwissenschaftlichen Analyse zentraler gesellschaftlicher Mechanismen vor dem Hintergrund (1) der Leitidee Nachhaltiger Entwicklung und (2) der Tatsache einer "Globalen Grossen Transformation" (wirtschaftlich, politisch, ökologisch und technisch).

Methodisches Wissen:
Die Studierenden werden vertraut gemacht mit ausgewählten Diskursen und Analyseansätzen aus den Bereichen Ökologische Ökonomie, Theorie der gesellschaftlichen Entwicklung, Institutionentheorie, Innovationstheorie, Welthandelslehre, Theorie einer menschenrechtsbasierten, liberalen Wirtschafts- und Gesellschaftspolitik.

Vermittelte Fähigkeiten:
1) Zielwissen: Die Studierenden werden mit Idee und Deutungsspektrum des Begriffs „Nachhaltige Entwicklung“ vertraut gemacht und in die Lage versetzt, sich kreativ in den aktuellen Nachhaltigkeitsdiskurs einzubringen. Hierzu gehört auch die Fähigkeit, die nachhaltigkeitsrelevanten Fragen im eigenen Fachgebiet zu identifizieren und zu erarbeiten. Motto: "Das Richtige tun, nicht das Überholte nachbessern!"

2) Analysewissen: Die Veranstaltung legt Grundlagen, die die Studierenden als Akteure in Wirtschaft, Politik und Gesellschaft in die Lage versetzen, reflektiert die tieferen Ursachen der heutigen Nichtnachhaltigkeit zu verstehen und zu erkennen, dass wir mitten in einer Globalen Grossen Transformation stecken - mit ihren Chancen und Gefahren.

3) Transformationswissen: Die Veranstaltung öffnet den Blick auf notwendige innovative Lösungsstrategien in den Bereichen Wirtschaft / Unternehmen, Politik, Zivilgesellshaft - jenseits von kurzsichtigem Pragmatismus und Symptombekämpfung.
InhaltKurzes Nachhaltigkeits-Update:
Ursprünge der Leitidee Nachhaltige Entwicklung, normative Grundlagen, Konzepte. Was bleibt gültig nach 25 Jahren Nachhaltigkeitsdiskurs?

Entwicklung als Freiheit:
Woran hängt es, dass Gesellschaften sich entwickeln und neue Wege beschreiten oder aber scheitern? Grundlagen einer Theorie der gesellschaftlichen Entwicklung, auf der Basis der Werke von Amartya Sen (2002), Daron Acemoglu / James A. Robinson (2013) und Jared Diamond (2005), unter Berücksichtigung u.a. von K.R. Popper, F.A.v. Hayek, R. Dahrendorf.

Konzeptionelle Grundlagen der Marktwirtschaft:
Die Ideen der Klassiker Walter Eucken und Ludwig Erhard. Was wurde daraus in den letzten 50 Jahren? Wie kann die Marktwirtschaft zukunftsfähig gemacht werden? Was wäre eine "zivilisierte Marktwirtschaft" (Peter Ulrich)?

Das "Neomerkantilismus-Syndrom":
Wie eine Politik der billigen Zentralressourcen, des billigen Geldes und der asymmetrischen Globalisierung uns in den letzten 50 Jahren Wohlstand brachte - und an die ökologischen und gesellschaftlichen Grenzen führte.

Wachstumskritik 2016:
Neuere Positionen zur Wachstumsfrage: "Die Wachstumsspirale: Geld, Energie und Imagination in der Dynamik des Marktprozesses" (H.C. Binswanger), "Prosperität ohne Wachstum?" (T. Jackson), "Intelligent wachsten!" (R. Fücks)

"Das Internet der Dinge":
Zu einem neuen Trend, der das Zeug hat, das Wirtschaftsleben grundlegend zu verändern - Tatsachen, Reflexionen, Perspektiven

Suffizienz:
Perspektiven einer resourcenleichten Gesellschaft

"Unternehmung 2020":
Umweltmanagement und CSR in Ehren, aber es braucht mehr: Zur Unternehmens-DNA der Zukunft (P. Sukhdev)

Zur Anatomie der Finanz- und Verschuldungskrise:
Ein aktueller Zwischenbericht zu einer fast unendlichen Geschichte - mit Bezügen zur ökologischen und sozialen Frage

Globalisierung:
Tatsachen und Reflexionen zu einem globalen Megatrend. Grundlagen einer fairen Globalisierung. Wie lässt sich ein Komplexphänomen wie die Globalisierung eigentlich gestalten?

"Fluch der Ressourcen":
Ressourcenreichtum kann arm machen. Zu den Zusammenhängen zwischen Ressourcenvorkommen, Ressourcenzugang, Demokratie und wirtschaftlicher Entwicklung, dargestellt und diskutiert anhand ausgewählter Länderbeispiele. Fluch der Ressourcen auch in der Schweiz?

Auf die Institutionen kommt es an!
Institutionentheoretische Grundlagen zur Gestaltung gesellschaftlicher Mechanismen. Überblick und Reflexion über das "Universum" konkreter institutioneller Innovationen für eine Nachhaltige Entwicklung in Zeiten grundlegender Transformationen. Im Grunde müssen wir Demokratie und Marktwirtschaft neu erfinden - oder: Lasst uns an den "Federalist Papers" weiterschreiben!

Prolog zur Synthese:
Die Erste Industrielle Revolution. Welches waren die wichtigsten Wirkungszusammenhänge und welches war das zugrunde liegende "Energie-Kommunikations-Mobilitäts-System"? Was ist heute ähnlich, was anders? Lehren

Synthese:
Die Grosse Globale Transformation ist Realität - man muss sie nur erkennen! Umrisse des sich abzeichnenden neuen "Energie-Kommunikations-Mobilitäts-Systems". Vor diesem Hintergrund: Zusammenführung der Inhalte der LV, Perspektiven & weiterführende Fragen
SkriptSkriptum und Zusatzunterlagen werden in der Lehrveranstaltung abgegeben
LiteraturEine erste Auswahl:
- Daron Acemoglu / James A. Robinson (2013): Warum Nationen scheitern. Die Ursprünge von Macht, Wohlstand und Armut, Frankfurt am Main
- Hans Christoph Binswanger (2006): Die Wachstumsspirale. Geld, Energie und Imagination in der Dynamik des Marksprozesses, Marburg
- Ralf Dahrendorf ( 2003): Auf der Suche nach einer neuen Ordnung, München
- Jared Diamond (2006): Kollaps - Warum Gesellschaften überleben oder untergehen. Frankfurt am Main (Amerikanische Originalausgabe: Collapse: How Societies Choose to Fail or Succeed, New York 2005)
- Ralf Fücks (2013): Intelligent wachsten, Die grüne Revolution, München
- Friedrich A. von Hayek (1991): Die Verfassung der Freiheit, 3. Auflage, Tübingen
- Friedrich A. von Hayek (1972): Theorie komplexer Phänomene, Tübingen
- Tim Jackson (2009): Prosperity without Growth. Economics for a Finite Planet, London
- Jürg Minsch / Peter H. Feindt / Hans. P. Meister / Uwe Schneidewind / Tobias Schulz (1998): Institutionelle Reformen für eine Politik der Nachhaltigkeit, Berlin / Heidelberg / New York
- J. Minsch / A. Eberle / B. Meier / U. Schneidewind (1996). Mut zum ökologischen Umbau. Innovationsstrategien für Unternehmen, Politik und Akteurnetze, Birkhäuser, Basel / Boston / Berlin
- Elinor Ostrom (1990): Die Verfassung der Allmende, Tübingen (Amerikanische Originalausgabe: Governing the Commons, Cambridge University Press, Cambridge / New York / Melbourne 1990)
- oekom e.V., Hrsg. (2013): Baustelle Zukunft. Die Grosse Trasformation von Wirtschaft und Gesellschaft, oekom Verlag, München
- Karl Polanyi (1978): The Great Transformation. Politische und ökonomische Ursprünge von Gesellschaften und Wirtschaftssystemen, suhrkamp Verlag, Frankfurt (Originalausgabe (1944): The Great Transformation)
- Karl. R. Popper (1980): Die offene Gesellschaft und ihre Feinde, Bde. I und II, 6. Auflage, Tübingen
- Jeremy Rifkin (2014): Die Null Grenzkosten Gesellschaft. Das Internet der Dinge, Kollaboratives Gemeingut und der Rückzug des Kapitalismus, Campus, Frankfurt am Main
- Uwe Schneidewind / Angelika Zahrnt (2013): Damit gutes Leben einfacher wird. Perspektiven einer Suffizienzpolitik, München
- Pavan Sukhdev (2013): Corporation 2020. Warum wir Wirtschaft neu denken müssen, München
- Tomas Sedlacek (2012): Die Ökonomie von Gut und Böse, München
- Amartya Sen (2002): Ökonomie für den Menschen. Wege zur Gerechtigkeit und Solidarität in der Marktwirtschaft, München (Amerikanische Originalausgabe: Development as Freedom, New York 1999)
-Daniel Spreng /Thomas Flüeler /David Goldblatt /Jürg Minsch (2012): Tackling Long Term Global Energy Problems: The Contribution of Social Science, Dortrecht / Heidelberg / New York
- Joseph Stiglitz (2006): Die Chancen der Globalisierung, München (Amerikanische Originalausgabe: Making Globalization Work, New York 2006)
- Peter Ulrich (2005): Zivilisierte Marktwirtschaft, 2. Aufl., Freiburg
- WBGU Wissenschaftlicher Beirat der Bundesregierung Globale Umweltveränderungen (2011): Welt im Wandel. Gesellschaftsvertrag für eine Grosse Transformation, Zusammenfassung für Entscheidungsträger, WBGU, Berlin, http://www.wbgu.de

Weitere Angaben in der Vorlesung
Voraussetzungen / BesonderesErwartet wird die Bereitschaft zur individuellen vertiefenden Auseinandersetzung mit der behandelten Thematik und die aktive Teilnahme an den Diskussionen
Umweltnaturwissenschaften Master Information
Vertiefung in Atmosphäre und Klima
Hydrologie und Wasserkreislauf
NummerTitelTypECTSUmfangDozierende
701-1253-00LAnalysis of Climate and Weather Data Information W3 KP2GC. Frei
KurzbeschreibungObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
LernzielObservation networks and numerical climate and forcasting models deliver large primary datasets. The use of this data in practice and in research requires specific techniques of statistical data analysis. This lecture introduces a range of frequently used techniques, and enables students to apply them and to properly interpret their results.
InhaltIntroduction into the theoretical background and the practical application of methods of data analysis in meteorology and climatology.

Topics: exploratory methods, hypothesis testing, analysis of climate trends, measuring the skill of climate and forecasting models, analysis of extremes, principal component analysis and maximum covariance analysis.

The lecture also provides an introduction into R, a programming language and graphics tool frequently used for data analysis in meteorology and climatology. During hands-on computer exercises the student will become familiar with the practical application of the methods.
SkriptDocumentation and supporting material include:
- documented view graphs used during the lecture
- excercise sets and solutions
- R-packages with software and example datasets for exercise sessions

All material is made available via the lecture web-page.
LiteraturSuggested literature:
- Wilks D.S., 2005: Statistical Methods in the Atmospheric Science. (2nd edition). International Geophysical Series, Academic Press Inc. (London)
- Coles S., 2001: An introduction to statistical modeling of extreme values. Springer, London. 208 pp.
Voraussetzungen / BesonderesPrerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren.
Vertiefung in Ökologie und Evolution
C. Wissenschaftliche Kompetenzen
Fachkenntnisse zu quantitativen und rechnerischen Verfahren
NummerTitelTypECTSUmfangDozierende
701-1419-00LAnalysis of Ecological DataW3 KP2GS. Güsewell
KurzbeschreibungThis class provides students with an overview of techniques for data analysis used in modern ecological research, as well as practical experience in running these analyses with R and interpreting the results. Topics include linear models, generalized linear models, mixed models, model selection and randomization methods.
LernzielStudents will be able to:
- describe the aims and principles of important techniques for the analysis of ecological data
- choose appropriate techniques for given problems and types of data
- evaluate assumptions and limitations
- implement the analyses in R
- represent the relevant results in graphs, tables and text
- interpret and evaluate the results in ecological terms
Inhalt- Linear models for experimental and observational studies
- Model selection
- Introduction to likelihood inference and Bayesian statistics
- Analysis of counts and proportions (generalised linear models)
- Models for non-linear relationships
- Grouping and correlation structures (mixed models)
- Randomisation methods
SkriptLecture notes and additional reading will be available electronically a few days before the course
LiteraturSuggested books for additional reading (available electronically)
Zuur A, Ieno EN & Smith GM (2007) Analysing ecological data. Springer, Berlin.
Zuur A, Ieno EN, Walker NJ, Saveliev AA & Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York.
Faraway JJ (2006) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis.
Voraussetzungen / BesonderesTime schedule
The course takes place on Mondays 12:45-15:00 from 25 September until 27 November, with the final exam on Monday 4 December. The last two weeks of the semester are free.

Prerequisites
- Basic statistical training (e.g. Mathematik IV in D-USYS): Data distributions, descriptive statistics, hypothesis testing, linear regression, analysis of variance
- Basic experience in data handling and data analysis in R

Individual preparation
Students without the required knowledge are asked to contact the lecturer before the first lecture date for support with individual preparation.
Vertiefung in Umweltsysteme und Politikanalyse
Modellierung und statistische Datenanalyse
NummerTitelTypECTSUmfangDozierende
860-0002-00LQuantitative Policy Analysis and ModelingO6 KP4GA. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet
KurzbeschreibungThe lectures will introduce students to the principles of quantitative policy analysis, namely the methods to predict and evaluate the social, economic, and environmental effects of alternative strategies to achieve public objectives. A series of graded assignments will give students an opportunity for students to apply those methods to a set of case studies
LernzielThe objectives of this course are to develop the following key skills necessary for policy analysts:
- Identifying the critical quantitative factors that are of importance to policy makers in a range of decision-making situations.
- Developing conceptual models of the types of processes and relationships governing these quantitative factors, including stock-flow dynamics, feedback loops, optimization, sources and effects of uncertainty, and agent coordination problems.
- Develop and program numerical models to simulate the processes and relationships, in order to identify policy problems and the effects of policy interventions.
- Communicate the findings from these simulations and associated analysis in a manner that makes transparent their theoretical foundation, the level and sources of uncertainty, and ultimately their applicability to the policy problem.
The course will proceed through a series of policy analysis and modeling exercises, involving real-world or hypothetical problems. The specific examples around which work will be done will concern the environment, energy, health, and natural hazards management.
Ergänzungen
Ergänzung in Nachhaltige Energienutzung
NummerTitelTypECTSUmfangDozierende
227-1631-00LEnergy System Analysis Information W4 KP3GG. Hug, S. Hellweg, F. Noembrini, A. Schlüter
KurzbeschreibungThe course provides an introduction to the methods and tools for analysis of energy consumption, energy production and energy flows. Environmental aspects are included as well as economical considerations. Different sectors of the society are discussed, such as electric power, buildings, and transportation. Models for energy system analysis planning are introduced.
LernzielThe purpose of the course is to give the participants an overview of the methods and tools used for energy systems analysis and how to use these in simple practical examples.
InhaltThe course gives an introduction to methods and tools for analysis of energy consumption, energy production and energy flows. Both larger systems, e.g. countries, and smaller systems, e.g. industries, homes, vehicles, are studied. The tools and methods are applied to various problems during the exercises. Different conventions of energy statistics used are introduced.

The course provides also an introduction to energy systems models for developing scenarios of future energy consumption and production. Bottom-up and Top-Down approaches are addressed and their features and applications discussed.

The course contains the following parts:
Part I: Energy flows and energy statistics
Part II: Environmental impacts
Part III: Electric power systems
Part IV: Energy in buildings
Part V: Energy in transportation
Part VI: Energy systems models
SkriptHandouts
LiteraturExcerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für Master-Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
701-0071-AALMathematics III: Systems Analysis
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RN. Gruber
KurzbeschreibungIn der Systemanalyse geht es darum, durch ausgesuchte praxisnahe Beispiele die in der Mathematik bereit gestellte Theorie zu vertiefen und zu veranschaulichen. Konkret behandelt werden: Dynamische lineare Boxmodelle mit einer und mehreren Variablen; Nichtlineare Boxmodelle mit einer oder mehreren Variablen; zeitdiskrete Modelle, und kontinuierliche Modelle in Raum und Zeit.
LernzielErlernen und Anwendung von Konzepten (Modellen) und quantitativen Methoden zur Lösung von umweltrelevanten Problemen. Verstehen und Umsetzen des systemanalytischen Ansatzes, d.h. Erkennen des Kernes eines Problemes - Abstraktion - Quantitatives Erfassen - Vorhersage.
Inhalthttp://www.up.ethz.ch/education/systems-analysis.html
SkriptFolien werden über Ilias zur Verfügung gestellt.
LiteraturImboden, D. and S. Koch (2003) Systemanalyse - Einführung in die mathematische Modellierung natürlicher Systeme. Berlin Heidelberg: Springer Verlag.
701-1901-AALSystems Analysis
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RN. Gruber
KurzbeschreibungSystems analysis is about the application of mathematical concepts to solve real world problems in a quantitative manner. Areas covered include: Dynamic linear models with one and several variables, Non-linear models with one or several variables; discrete-time models; and continuous models in space and time.
LernzielThe goal of the course is to develop quantitative skills in order to understand and solve a range of typical environmental problems.
InhaltThe subject of the exam is the content of my
undergraduate lecture series Systemanalyse I and II (see http://www.up.ethz.ch/education/system_analysis/index_DE).
This course is closely aligned with the Imboden&Koch / Imboden&Pfenniger books, except that I essentially skip chapter 7.
SkriptNo script is available, but you can purchase the Imboden/Koch or Imboden/Pfenniger books (or download some of the chapters yourself) through the Springer Verlag:

English version:
http://link.springer.com/book/10.1007/978-3-642-30639-6/page/1

German version:
http://www.springer.com/environment/book/978-3-540-43935-6