Search result: Catalogue data in Autumn Semester 2017
Agricultural Sciences Bachelor | ||||||
Bachelor Studies (Programme Regulations 2016) | ||||||
1. Semester | ||||||
First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|
401-0251-00L | Mathematics I | O | 6 credits | 4V + 2U | L. Halbeisen | |
Abstract | This course covers mathematical concepts and techniques necessary to model, solve and discuss scientific problems - notably through ordinary differential equations. | |||||
Objective | Mathematics is of ever increasing importance to the Natural Sciences and Engineering. The key is the so-called mathematical modelling cycle, i.e. the translation of problems from outside of mathematics into mathematics, the study of the mathematical problems (often with the help of high level mathematical software packages) and the interpretation of the results in the original environment. The goal of Mathematics I and II is to provide the mathematical foundations relevant for this paradigm. Differential equations are by far the most important tool for modelling and are therefore a main focus of both of these courses. | |||||
Content | 1. Single-Variable Calculus: review of differentiation, linearisation, Taylor polynomials, maxima and minima, antiderivative, fundamental theorem of calculus, integration methods, improper integrals. 2. Linear Algebra and Complex Numbers: systems of linear equations, Gauss-Jordan elimination, matrices, determinants, eigenvalues and eigenvectors, cartesian and polar forms for complex numbers, complex powers, complex roots, fundamental theorem of algebra. 3. Ordinary Differential Equations: separable ordinary differential equations (ODEs), integration by substitution, 1st and 2nd order linear ODEs, homogeneous systems of linear ODEs with constant coefficients, introduction to 2-dimensional dynamical systems. | |||||
Literature | - Thomas, G. B.: Thomas' Calculus, Part 1 (Pearson Addison-Wesley). - Bretscher, O.: Linear Algebra with Applications (Pearson Prentice Hall). | |||||
Prerequisites / Notice | Prerequisites: familiarity with the basic notions from Calculus, in particular those of function and derivative. Mathe-Lab (Assistance): Mondays 12-14, Tuesdays 17-19, Wednesdays 17-19, in Room HG E 41. | |||||
Basic Courses (Second Year) | ||||||
Examination Block | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-00L | Mathematics III: Systems Analysis | O | 4 credits | 2V + 1U | N. Gruber, M. Vogt | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
Bachelor Studies (Programme Regulations 2010) | ||||||
5. Semester | ||||||
Methodical Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
751-0441-00L | Scientific Analysis and Presentation of Data | O | 2 credits | 2G | W. Eugster | |
Abstract | This lecture gives an introduction to the scientific work with data covering all steps from data entry via statistical analyses to producing correct scientific graphical output. Exercises with the data analysis software R (via RStudio) will provide hands-on opportunities to get acquainted with data analysis and presentation. Field data gathered with Prof. E. Frossard will be used. | |||||
Objective | This lecture with exercises gives an introduction to the scientific work with data, starting with data acquisition and ending with statistical analyses as they are often required for a bachelor thesis (descriptive statistics, linear regression etc.). Getting data organized with a spreadsheet program (LibreOffice, Excel) and then transfering them to the open-source R package will be the primary focus. An imporant aspect will be to learn which graphical representation of data are best suited for the task (how can data be presented clearly and still scientifically correct?) | |||||
Content | Tentative Programme: 1. Introduction 2. Data acquisition, data organization, data storage, working with data 3. Graphical presentations I - Spreadsheets 4. Preparation of own data from field course with Prof. E. Frossard / 4. Sem. 5. Correct and problematic graphical data displays 6. Introduction to 'R' 7. Data import and graphical presentation 8. Statistical distribution and confidence intervals 9. Statistical tests - Repetition and hands-on applications 10. Linear regressions 11./12. Analysis of Variance 13. ANOVA - Discussion of results with Prof. E. Frossard Last week of semester: examination (Leistungskontrolle) | |||||
Lecture notes | Mainly German (with some English passages from text books) | |||||
Prerequisites / Notice | Theoretical background in ensemble statistics from the mandatory course in the 4th semester; students should have cleared the examination of that fundamental course to be able to follow | |||||
Agroecosystem Sciences Master | ||||||
Master Studies (Programme Regulations 2016) | ||||||
Major in Agriculture Economics | ||||||
Methodology Competences | ||||||
Methods in Agricultural Economics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
751-0423-00L | Risk Analysis and Risk Management in Agriculture | W+ | 3 credits | 2G | R. Finger | |
Abstract | Agricultural 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. | |||||
Objective | -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 | |||||
Content | - 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 | |||||
Lecture notes | Handouts will be distributed in the lecture and available on the moodle. | |||||
Prerequisites / Notice | knowledge of basic concepts of probability theory and microeconomics | |||||
Minors | ||||||
Agricultural Economics and Policy | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
751-0423-00L | Risk Analysis and Risk Management in Agriculture | W | 3 credits | 2G | R. Finger | |
Abstract | Agricultural 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. | |||||
Objective | -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 | |||||
Content | - 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 | |||||
Lecture notes | Handouts will be distributed in the lecture and available on the moodle. | |||||
Prerequisites / Notice | knowledge of basic concepts of probability theory and microeconomics | |||||
Master Studies (Programme Regulations 2011) | ||||||
Majors | ||||||
Major in Food and Resource Use Economics | ||||||
Methodology Competences | ||||||
Methods in Food and Resource Use Economics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
751-0423-00L | Risk Analysis and Risk Management in Agriculture | W+ | 3 credits | 2G | R. Finger | |
Abstract | Agricultural 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. | |||||
Objective | -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 | |||||
Content | - 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 | |||||
Lecture notes | Handouts will be distributed in the lecture and available on the moodle. | |||||
Prerequisites / Notice | knowledge of basic concepts of probability theory and microeconomics | |||||
Atmospheric and Climate Science Master | ||||||
Modules | ||||||
Hydrology and Water Cycle | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-1253-00L | Analysis of Climate and Weather Data | W | 3 credits | 2G | C. Frei | |
Abstract | Observation 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. | |||||
Objective | Observation 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. | |||||
Content | Introduction 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. | |||||
Lecture notes | Documentation 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. | |||||
Literature | Suggested 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. | |||||
Prerequisites / Notice | Prerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren. | |||||
Minors | ||||||
Minor in Sustainable Energy Use | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-1631-00L | Energy System Analysis | W | 4 credits | 3G | G. Hug, S. Hellweg, F. Noembrini, A. Schlüter | |
Abstract | The 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. | |||||
Objective | The 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. | |||||
Content | The 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 | |||||
Lecture notes | Handouts | |||||
Literature | Excerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8 | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-AAL | Mathematics III: Systems Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | N. Gruber | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
701-1901-AAL | Systems Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | N. Gruber | |
Abstract | Systems 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. | |||||
Objective | The goal of the course is to develop quantitative skills in order to understand and solve a range of typical environmental problems. | |||||
Content | The subject of the exam is the content of my undergraduate lecture series Systemanalyse I and II (see Link). This course is closely aligned with the Imboden&Koch / Imboden&Pfenniger books, except that I essentially skip chapter 7. | |||||
Lecture notes | No 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: Link German version: Link | |||||
Civil Engineering Bachelor | ||||||
Bachelor Studies (Programme Regulations 2014) | ||||||
First Year Compulsory Courses | ||||||
First Year Examinations In place of the German course 851-0703-03L Introduction to Law for Civil Engineering students can take the French course 851-0709-00L Droit civil. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0241-00L | Analysis I | O | 7 credits | 5V + 2U | M. Akka Ginosar | |
Abstract | Mathematical tools for the engineer | |||||
Objective | Mathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers. | |||||
Content | Complex numbers. Calculus for functions of one variable with applications. Simple Mathematical models in engineering. | |||||
Lecture notes | Die Vorlesung folgt weitgehend Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer; online verfügbar unter: Link | |||||
Literature | Neben 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: Link Meike Akveld, "Analysis 1", vdf; Link Urs Stammbach, "Analysis I/II" (erhältlich im ETH Store); Link | |||||
Compulsory Courses 3. Semester | ||||||
Examination Block 1 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0243-00L | Analysis III | O | 3 credits | 2V + 1U | A. Sisto | |
Abstract | We 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. | |||||
Objective | Learning 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. | |||||
Content | Classification 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. | |||||
Literature | The course material is taken from the following sources: Stanley J. Farlow - Partial Differential Equations for Scientists and Engineers G. Felder: Partielle Differenzialgleichungen. Link | |||||
Prerequisites / Notice | Analysis I and II. In particular, knowing how to solve ordinary differential equations is an important prerequisite. | |||||
Civil Engineering Master | ||||||
1. Semester | ||||||
Major Courses | ||||||
Major in Structural Engineering | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0187-00L | Structural Reliability and Risk Analysis | W | 3 credits | 2G | S. Marelli | |
Abstract | Structural 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. | |||||
Objective | The 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. | |||||
Content | Engineers 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. | |||||
Lecture notes | Slides 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. | |||||
Literature | Ang, 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. | |||||
Prerequisites / Notice | Basic course on probability theory and statistics | |||||
3. Semester | ||||||
Major Courses | ||||||
Major in Construction and Maintenance Management | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0439-00L | Introduction 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". | W | 6 credits | 4G | K. W. Axhausen, R. Schubert | |
Abstract | The course presents basic economic principles as well as cost benefit analyses in transport; it also introduces methods used to derive the monetary values of non-market goods. | |||||
Objective | Familiarity with basic microeconomic and macroeconomic principles and with the essential methods of project appraisal | |||||
Content | Basic microeconomic and macroeconomic üpronciples; Cost-Benefit-Analyses; multi-criteria analyses; European guidelines; stated response methods; travel cost approach and others; Valuation of travel time savings; valuation of traffic safety | |||||
Lecture notes | moodle platform for the basic economic principles; handouts | |||||
Literature | Taylor, 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. | |||||
Major in Structural Engineering | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0179-00L | Probabilistic Seismic Risk Analysis and Management for Civil Systems Does not take place this semester. | W | 3 credits | 2G | B. Stojadinovic, to be announced | |
Abstract | Advanced 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. | |||||
Objective | After 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. | |||||
Content | This 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. | |||||
Lecture notes | The 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. | |||||
Literature | Reading 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. Link - SAP2000 v15.1: general-purpose 3D nonlinear structural analysis software. Link - OpenSees: Open System for Earthquake Engineering Simulation, is an object-oriented, open- source software framework. Link | |||||
Prerequisites / Notice | ETH 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. | |||||
Major in Transport Systems | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0439-00L | Introduction 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". | W | 6 credits | 4G | K. W. Axhausen, R. Schubert | |
Abstract | The course presents basic economic principles as well as cost benefit analyses in transport; it also introduces methods used to derive the monetary values of non-market goods. | |||||
Objective | Familiarity with basic microeconomic and macroeconomic principles and with the essential methods of project appraisal | |||||
Content | Basic microeconomic and macroeconomic üpronciples; Cost-Benefit-Analyses; multi-criteria analyses; European guidelines; stated response methods; travel cost approach and others; Valuation of travel time savings; valuation of traffic safety | |||||
Lecture notes | moodle platform for the basic economic principles; handouts | |||||
Literature | Taylor, 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. | |||||
Biology Bachelor | ||||||
2. Year, 3. Semester | ||||||
Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
551-1003-00L | Methods of Biological Analysis | O | 3 credits | 3G | R. Aebersold, M. Badertscher, K. Weis | |
Abstract | 529-1042-00 Principles of the most important separation techniques and the interpretation of molecular spectra. 551-1003-00 The course will teach the basis and typical applications of methods for the analysis of nucleic acid sequences, mass spectrometric analysis of proteins and proteomes and advanced light and fluorescent imaging methods. | |||||
Objective | 529-1042-00 Knowledge of the necessary basics and the possibilities of application of the relevant spectroscopical and separation methods in analytical chermistry. 551-1003-00 Knowledge of the theoretical basis of the methods for nucleic acid sequence analysis, mass spectrometry based protein and proteome analysis and advanced light and fluorescent imaging methods, and an understanding of the application of these principles in experimental biology. | |||||
Content | 529-1042-00 Application oriented basics of instrumental analysis in organic chemistry and the empirical employment of the methods of structure elucidation (mass spectrometry, NMR-, IR-, UV/VIS spectroscopy). Basics and application of chromatographic and electrophoretic separation methods. Application of the knowledge by practising. 551-1003-00 The course will consist of lectures covering the theoretical and technical base of the respective analytical methods and of exercises where typical applications of the methods in modern experimental biology are discussed. | |||||
Lecture notes | 529-1042-00 A comprehensive script is available in the HCI-Shop. A summary of the part "Spektroskopie" defines the relevant material for the exam. 551-1003-00 Materials supporting the lectures and exercises will be made available via Moodle. | |||||
Literature | 529-1042-00 - 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; | |||||
Prerequisites / Notice | 529-1042-00 Prerequisites: - 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. Year, 5. Semester | ||||||
Block Courses Registration for Block courses is mandatory. Please register under Link . Registration period: from 24.7.2017 to 6.8.2017. | ||||||
Block Courses in 2nd Quarter of the Semester From 12.10.2017 08:00 Uhr to 3.11.2017 17:00 hr | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
551-1201-00L | Computational Methods in Genome and Sequence Analysis Number of participants limited to 5. The enrolment is done by the D-BIOL study administration. | W | 6 credits | 7G | A. Wutz | |
Abstract | This 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. | |||||
Objective | Methods 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. | |||||
Content | •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. | |||||
Prerequisites / Notice | - It is recommended to bring your own computer with a Python installation to the course - simple computers can be provided - Programming basics with Python | |||||
Block Courses in 4th Quarter of the Semester From 30.11.2017 08:00 hr to 22.12.2017 17:00 hr | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
551-1417-00L | In Vivo Cryo-EM Analysis of Dynein Motor Proteins Number of participants limited to 3. The enrolment is done by the D-BIOL study administration. | W | 6 credits | 7G | T. Ishikawa | |
Abstract | Motor 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. | |||||
Objective | The 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. | |||||
Content | Motor 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. | |||||
Lecture notes | Scripts will be distributed during the course. | |||||
Literature | An 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. | |||||
Biology Master | ||||||
Elective Major Subject Areas | ||||||
Elective Major: Ecology and Evolution | ||||||
Elective Compulsory Master Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R efficiently for data analysis. | |||||
Content | The 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: Link | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | Basic 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 Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
701-1419-00L | Analysis of Ecological Data | W | 3 credits | 2G | S. Güsewell | |
Abstract | This 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. | |||||
Objective | Students 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 | |||||
Content | - 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 | |||||
Lecture notes | Lecture notes and additional reading will be available electronically a few days before the course | |||||
Literature | Suggested 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. | |||||
Prerequisites / Notice | Time 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. | |||||
Elective Major: Structural Biology and Biophysics | ||||||
Elective Compulsory Master Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
Biomedical Engineering Master | ||||||
Track Courses | ||||||
Bioelectronics | ||||||
Recommended Elective Courses These courses are particularly recommended for the Bioelectronics track. Please consult your track advisor if you wish to select other subjects. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Bioimaging | ||||||
Track Core Courses During the Master program, a minimum of 12 CP must be obtained from track core courses. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Recommended Elective Courses These courses are particularly recommended for the Bioimaging track. Please consult your track advisor if you wish to select other subjects. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0391-00L | Medical Image Analysis Does not take place this semester. | W | 3 credits | 2G | E. Konukoglu | |
Abstract | It 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. | |||||
Objective | This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas. | |||||
Prerequisites / Notice | Basic knowledge of computer vision would be helpful. | |||||
227-0969-00L | Methods & Models for fMRI Data Analysis | W | 6 credits | 4V | K. Stephan | |
Abstract | This 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. | |||||
Objective | To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their application to empirical fMRI data. | |||||
Content | This 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 | ||||||
Track Core Courses During the Master program, a minimum of 12 CP must be obtained from track core courses. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Medical Physics | ||||||
Other Elective Courses These courses may be suitable for the Medical Physics track. Please consult your track advisor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Biotechnology Master | ||||||
Master Studies (Programme Regulations 2017) | ||||||
Practical Training Students need to acquire a total of 14 ECTS in lab courses. All listed lab courses are mandatory. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
636-0201-00L | Lab Course: Methods in Cell Analysis and Laboratory Automation Only for Biotechnology MSc, Programme Regulations 2017. | O | 2 credits | 6P | T. Horn | |
Abstract | The 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. | |||||
Objective | -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 | |||||
Content | The 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 | |||||
Lecture notes | You will find further information on the practical course and the equipment at: Link Link | |||||
Literature | Microscopy: 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 | |||||
Prerequisites / Notice | The 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 Applied Statistics Course duration: about 12 months Next course: FS 2019 | ||||||
Compulsory Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
447-0625-01L | Applied Analysis of Variance and Experimental Design I Only for DAS and CAS in Applied Statistics. | O | 3 credits | 1V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Further Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
447-0625-02L | Applied Analysis of Variance and Experimental Design II Only for DAS and CAS in Applied Statistics. | Z | 3 credits | 1V + 1U | L. Meier | |
Abstract | Random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power. | |||||
Objective | Participants 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
CAS in Development and Cooperation | ||||||
Module | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
865-0000-06L | Impact Analysis: Methods and Applications. Only for MAS/CAS in Development and Cooperation students, as well as specialists with at least 24 months of practical experience in international cooperation. Doctoral students dealing with empirical research in the area of development and cooperation (EZA) may be admitted "sur Dossier". Registration only through the NADEL administration office. | W | 2 credits | 3G | I. Günther | |
Abstract | The course gives an introduction to the most important methods for rigorous impact analysis of development programs and projects. The course is designed to both cover the most fundamental methods of impact analysis and introduce real world case studies from national, international and non-governmental development organizations and asks how rigorous impact analysis has influenced their policies. | |||||
Objective | Participants understand the most important methods of impact analysis. They are able to conduct small scale studies to evaluate the impact of their own programs as well as manage larger impact evaluations for their organizations. Participants are able to use the results of own and external impact studies. | |||||
Content | Introduction to rigorous impact analysis; Case studies and their policy implications; Introduction to the required statistical knowledge; Potentials and limitations of quantitative analysis; Experimental and quasi-experimental methods; Relevant and feasible indicators for the measurement of outcomes and impacts; Data collection and analysis; Project management of an impact analysis. | |||||
Prerequisites / Notice | Students of the course must fulfil requirements specified on the homepage of NADEL. Electronic registration may be done only after registration with NADEL secretariate. | |||||
Chemistry Bachelor | ||||||
1. Semester | ||||||
Compulsory Subjects First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0271-00L | Mathematical Foundations I: Analysis A | O | 5 credits | 3V + 2U | L. Kobel-Keller | |
Abstract | Introduction to calculus in one dimension. Building simple models and analysing them mathematically. Functions of one variable: the notion of a function, of the derivative, the idea of a differential equation, complex numbers, Taylor polynomials and Taylor series. The integral of a function of one variable. | |||||
Objective | Introduction to calculus in one dimension. Building simple models and analysing them mathematically. | |||||
Content | Functions of one variable: the notion of a function, of the derivative, the idea of a differential equation, complex numbers, Taylor polynomials and Taylor series. The integral of a function of one variable. | |||||
Literature | G. 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 further reading suggestions will be indicated during the lecture | |||||
Chemical Engineering Bachelor | ||||||
1. Semester | ||||||
Compulsory Subjects First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0271-00L | Mathematical Foundations I: Analysis A | O | 5 credits | 3V + 2U | L. Kobel-Keller | |
Abstract | Introduction to calculus in one dimension. Building simple models and analysing them mathematically. Functions of one variable: the notion of a function, of the derivative, the idea of a differential equation, complex numbers, Taylor polynomials and Taylor series. The integral of a function of one variable. | |||||
Objective | Introduction to calculus in one dimension. Building simple models and analysing them mathematically. | |||||
Content | Functions of one variable: the notion of a function, of the derivative, the idea of a differential equation, complex numbers, Taylor polynomials and Taylor series. The integral of a function of one variable. | |||||
Literature | G. 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 further reading suggestions will be indicated during the lecture | |||||
Computational Biology and Bioinformatics Master More informations at: Link | ||||||
Master Studies (Programme Regulations 2017) | ||||||
Core Courses 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 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: Link | W | 5 credits | 3G | H. Rehrauer, M. Robinson | |
Abstract | A 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. | |||||
Objective | -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 | |||||
Content | Lectures 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 | |||||
Lecture notes | Lecture notes, published manuscripts | |||||
Prerequisites / Notice | Prerequisites: 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 Studies (Programme Regulations 2011) | ||||||
Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: Link | W | 5 credits | 3G | H. Rehrauer, M. Robinson | |
Abstract | A 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. | |||||
Objective | -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 | |||||
Content | Lectures 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 | |||||
Lecture notes | Lecture notes, published manuscripts | |||||
Prerequisites / Notice | Prerequisites: 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 Applied Statistics Course duration: about 20 months Next course: FS 2019 | ||||||
Compulsory Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
447-0625-01L | Applied Analysis of Variance and Experimental Design I Only for DAS and CAS in Applied Statistics. | O | 3 credits | 1V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
447-0625-02L | Applied Analysis of Variance and Experimental Design II Only for DAS and CAS in Applied Statistics. | W | 3 credits | 1V + 1U | L. Meier | |
Abstract | Random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power. | |||||
Objective | Participants 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Data Science Master | ||||||
Core Courses | ||||||
Core Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Doctoral Department of Chemistry and Applied Biosciences Further information at: Link | ||||||
Doctoral and Post-Doctoral Courses | ||||||
Doctoral Studies in Inorganic Chemistry | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
529-0169-00L | Instrumental Analysis | E- | 0 credits | 2S | D. Günther | |
Abstract | Group seminar on elemental analysis and isotope ratio determinations using various plasma sources | |||||
Objective | ||||||
Content | Developments in plasma mass spectrometry and alternative plasma sources | |||||
Doctoral Department of Mechanical and Process Engineering More Information at: Link | ||||||
Doctoral and Post-Doctoral Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Doctoral Department of Mathematics | ||||||
Graduate School Official website of the Zurich Graduate School in Mathematics: Link | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4657-00L | Numerical Analysis of Stochastic Ordinary Differential Equations Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods" | W | 6 credits | 3V + 1U | A. Jentzen | |
Abstract | Course 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. | |||||
Objective | The 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. | |||||
Content | Generation 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 | |||||
Lecture notes | Lecture Notes are available in the lecture homepage (please follow the link in the Learning materials section). | |||||
Literature | P. 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. | |||||
Prerequisites / Notice | Prerequisites: 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. | |||||
401-4623-00L | Time Series Analysis Does not take place this semester. | W | 6 credits | 3G | not available | |
Abstract | Statistical 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. | |||||
Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Content | This 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. | |||||
Lecture notes | Not available | |||||
Literature | A list of references will be distributed during the course. | |||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||
Colloquia | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-5350-00L | Analysis Seminar | E- | 0 credits | 1K | M. Struwe, A. Carlotto, F. Da Lio, A. Figalli, N. Hungerbühler, T. Ilmanen, T. Kappeler, T. Rivière, D. A. Salamon | |
Abstract | Research colloquium | |||||
Objective | ||||||
Doctoral Department of Environmental Sciences More Information at: Link | ||||||
Environmental Sciences | ||||||
Atmosphere and Climate | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-1253-00L | Analysis of Climate and Weather Data | W | 3 credits | 2G | C. Frei | |
Abstract | Observation 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. | |||||
Objective | Observation 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. | |||||
Content | Introduction 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. | |||||
Lecture notes | Documentation 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. | |||||
Literature | Suggested 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. | |||||
Prerequisites / Notice | Prerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren. | |||||
Electrical Engineering and Information Technology Bachelor | ||||||
1. Semester | ||||||
First Year Examinations | ||||||
First Year Examination Block B | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0231-10L | Analysis I | O | 8 credits | 4V + 3U | T. H. Willwacher | |
Abstract | Calculus of one variable: Real and complex numbers, vectors, limits, sequences, series, power series, continuous maps, differentiation and integration in one variable, introduction to ordinary differential equations | |||||
Objective | Einfuehrung in die Grundlagen der Analysis | |||||
Lecture notes | Konrad Koenigsberger, Analysis I. Christian Blatter: Ingenieur-Analysis (Kapitel 1-3) | |||||
3. Semester | ||||||
Examination Blocks | ||||||
Examination Block 1 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0353-00L | Analysis III | O | 4 credits | 2V + 1U | A. Figalli | |
Abstract | In this lecture we treat problems in applied analysis. The focus lies on the simplest cases of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation and the wave equation. | |||||
Objective | ||||||
Content | 1.) 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) | |||||
Literature | Y. 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. Link | |||||
Prerequisites / Notice | Prerequisites: Analysis I and II, Fourier series (Komplexe Analysis) | |||||
Electrical Engineering and Information Technology Master | ||||||
Major Courses A total of 42 CP must be achieved during the Master Program. The individual study plan is subject to the tutor's approval. | ||||||
Communication | ||||||
Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0377-00L | Physics of Failure and Failure Analysis of Electronic Devices and Equipment | W | 3 credits | 2V | U. Sennhauser | |
Abstract | Failures 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. | |||||
Objective | Introduction 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. | |||||
Content | Summary 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 | |||||
Lecture notes | Comprehensive copy of transparencies | |||||
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: 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 | ||||||
Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0377-00L | Physics of Failure and Failure Analysis of Electronic Devices and Equipment | W | 3 credits | 2V | U. Sennhauser | |
Abstract | Failures 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. | |||||
Objective | Introduction 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. | |||||
Content | Summary 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 | |||||
Lecture notes | Comprehensive copy of transparencies | |||||
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: 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 | ||||||
Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0377-00L | Physics of Failure and Failure Analysis of Electronic Devices and Equipment | W | 3 credits | 2V | U. Sennhauser | |
Abstract | Failures 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. | |||||
Objective | Introduction 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. | |||||
Content | Summary 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 | |||||
Lecture notes | Comprehensive copy of transparencies | |||||
Energy and Power Electronics | ||||||
Core Subjects These core subjects are particularly recommended for the field of "Energy and Power Electronics". | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0526-00L | Power System Analysis | W | 6 credits | 4G | G. Hug | |
Abstract | The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks. | |||||
Objective | The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states. | |||||
Content | The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids. | |||||
Lecture notes | Lecture notes. | |||||
Systems and Control | ||||||
Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
227-0526-00L | Power System Analysis | W | 6 credits | 4G | G. Hug | |
Abstract | The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks. | |||||
Objective | The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states. | |||||
Content | The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids. | |||||
Lecture notes | Lecture notes. | |||||
Signal Processing and Machine Learning Coming soon! | ||||||
Core Subjects | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Subjects of General Interest | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0377-00L | Physics of Failure and Failure Analysis of Electronic Devices and Equipment | W | 3 credits | 2V | U. Sennhauser | |
Abstract | Failures 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. | |||||
Objective | Introduction 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. | |||||
Content | Summary 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 | |||||
Lecture notes | Comprehensive copy of transparencies | |||||
Energy Science and Technology Master | ||||||
Core Subjects | ||||||
Compulsory core courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-1631-00L | Energy System Analysis | W | 4 credits | 3G | G. Hug, S. Hellweg, F. Noembrini, A. Schlüter | |
Abstract | The 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. | |||||
Objective | The 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. | |||||
Content | The 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 | |||||
Lecture notes | Handouts | |||||
Literature | Excerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8 | |||||
Elective Core Courses 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. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0526-00L | Power System Analysis | W | 6 credits | 4G | G. Hug | |
Abstract | The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks. | |||||
Objective | The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states. | |||||
Content | The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids. | |||||
Lecture notes | Lecture notes. | |||||
Other Elective Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0360-00L | Procedures for the Analysis of Structures | W | 4 credits | 2V + 1U | G. Kress | |
Abstract | Basic theories for structure integrity calculations are presented with focus on strength, stability, fatigue and elasto-plastic structural analysis. Theories and models for one dimesional and planar structures are presented based on energy theorems. | |||||
Objective | Basic principles applied in structural mechanics. Introduction to the theories of planar structures. Development of an understanding of the relationship between material properties, structural theories and design criteria. | |||||
Content | 1. Basic problem of continuum mechanics and energy principles: structural theories, homogenization theories; finite elements; fracture mechanics. 2.Structural theories for planar structures and stability: plane-stress, plate theory, buckling of plates (non-linear plate theory). 3.Strength of material theories and material properties: ductile behaviour, plasticity, von Mises, Tresca, principal stress criterion; brittle behaviour; viscoplastic behaviour, creep resistance. 4. Structural design: fatigue and dynamic structural analysis. | |||||
Lecture notes | Script and all other course material available on MOODLE | |||||
Prerequisites / Notice | none | |||||
Earth Sciences Bachelor | ||||||
Bachelor Studies (Programme Regulations 2016) | ||||||
1. Semester | ||||||
First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0251-00L | Mathematics I | O | 6 credits | 4V + 2U | L. Halbeisen | |
Abstract | This course covers mathematical concepts and techniques necessary to model, solve and discuss scientific problems - notably through ordinary differential equations. | |||||
Objective | Mathematics is of ever increasing importance to the Natural Sciences and Engineering. The key is the so-called mathematical modelling cycle, i.e. the translation of problems from outside of mathematics into mathematics, the study of the mathematical problems (often with the help of high level mathematical software packages) and the interpretation of the results in the original environment. The goal of Mathematics I and II is to provide the mathematical foundations relevant for this paradigm. Differential equations are by far the most important tool for modelling and are therefore a main focus of both of these courses. | |||||
Content | 1. Single-Variable Calculus: review of differentiation, linearisation, Taylor polynomials, maxima and minima, antiderivative, fundamental theorem of calculus, integration methods, improper integrals. 2. Linear Algebra and Complex Numbers: systems of linear equations, Gauss-Jordan elimination, matrices, determinants, eigenvalues and eigenvectors, cartesian and polar forms for complex numbers, complex powers, complex roots, fundamental theorem of algebra. 3. Ordinary Differential Equations: separable ordinary differential equations (ODEs), integration by substitution, 1st and 2nd order linear ODEs, homogeneous systems of linear ODEs with constant coefficients, introduction to 2-dimensional dynamical systems. | |||||
Literature | - Thomas, G. B.: Thomas' Calculus, Part 1 (Pearson Addison-Wesley). - Bretscher, O.: Linear Algebra with Applications (Pearson Prentice Hall). | |||||
Prerequisites / Notice | Prerequisites: familiarity with the basic notions from Calculus, in particular those of function and derivative. Mathe-Lab (Assistance): Mondays 12-14, Tuesdays 17-19, Wednesdays 17-19, in Room HG E 41. | |||||
General Courses in Earth Sciences | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4271-00L | Data Analysis and Visualisation with Matlab in Earth Sciences | O | 3 credits | 3G | S. Wiemer, G. De Souza, T. Tormann | |
Abstract | This lecture and the corresponding exercises provide the students with an introduction to the concepts and tools of scientific data analysis. Based on current questions in the Earth Sciences, the students solve problems of increasing complexity both in small groups and singly using the software package MATLAB. Students also learn how to effectively visualise different kinds of datasets. | |||||
Objective | The following concepts are introduced in the course: - Effective data analysis and visualisation in 2D and 3D - Working with matrices and arrays - Programming and development of algorithms - Learning to effectively use animations - Statistical description of a dataset - Interactive data-mining - Uncertainty, error propagation and bootstrapping - Regression analysis - Testing hypotheses | |||||
3. Semester | ||||||
Basic Courses II | ||||||
Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-00L | Mathematics III: Systems Analysis | O | 4 credits | 2V + 1U | N. Gruber, M. Vogt | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
Bachelor Studies (Programme Regulations 2010) | ||||||
5. Semester Majors | ||||||
Major in Climate and Water: Electives Advisor of the BSc-major "Climate and Water" is Dr. Erich Fischer, Institute for climate and atmosphere (IAC). | ||||||
Major in Climate and Water: Electives In addition to the mandatory seminar for Bachelor Students: Atmosphere and Climate (course nr. 701-0459-00 in autumn semester) another 22 credits must be acquired from the offered elective courses during the 5th and 6th semester. The choice of other courses has to be granted by the advisor (Dr. Erich Fischer). | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
Earth Sciences Master | ||||||
Major in Geology | ||||||
Compulsory Module in Analytical Methods in Earth Sciences Students have to complete 6 credits in part A, and 6 credits in part B. | ||||||
Part B: Methods | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4117-00L | Sediment Analysis | W+ | 3 credits | 2G | M. G. Fellin, A. Gilli, V. Picotti | |
Abstract | Theoretical background and application of some basic methods for sediment analysis. | |||||
Objective | The main goal is to learn how to apply the analysis of the texture and grain-size of sediments to constrain the sedimentary processes and environments. | |||||
Content | A one-day fieldtrip to a local outcrop to learn how to describe sediments in the field and to collect samples for grain-size and compositional analysis. Application of the same analytical techniques on samples of unknown origin: the sampling sites will be revealed at the end of the course. Discussion of the theoretical background and of the results in class. At the end of the course, the student will have to hand in a report with the presentation and discussion of all the data produced during the course. | |||||
Lecture notes | For the various analytical methods English texts will be provided in class. | |||||
Literature | Introduction to clastic sedimentology. R.J. Cheel | |||||
Open Choice Modules Geology | ||||||
Basin Analysis | ||||||
Basin Analysis: Compulsory Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4231-00L | Basin Analysis | W+ | 3 credits | 2G | S. Willett, T. I. Eglinton, M. Lupker | |
Abstract | The 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. | |||||
Objective | Based 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. | |||||
Content | The 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.). | |||||
Lecture notes | Lecture notes are provided online during the course. They summarize the current subjects week by week, and provide the essential theoretical background. | |||||
Literature | Main 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. | |||||
Prerequisites / Notice | Familiarity with MATLAB is advantageous, but not required. | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
406-0243-AAL | Analysis I and II Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 14 credits | 30R | M. Akka Ginosar | |
Abstract | Mathematical tools for the engineer | |||||
Objective | Mathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers. | |||||
Content | Complex 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. | |||||
Literature | Textbooks 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 | |||||
Geomatic Engineering and Planning Bachelor | ||||||
1. Semester | ||||||
First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0241-00L | Analysis I | O | 7 credits | 5V + 2U | M. Akka Ginosar | |
Abstract | Mathematical tools for the engineer | |||||
Objective | Mathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers. | |||||
Content | Complex numbers. Calculus for functions of one variable with applications. Simple Mathematical models in engineering. | |||||
Lecture notes | Die Vorlesung folgt weitgehend Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer; online verfügbar unter: Link | |||||
Literature | Neben 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: Link Meike Akveld, "Analysis 1", vdf; Link Urs Stammbach, "Analysis I/II" (erhältlich im ETH Store); Link | |||||
Geomatic Engineering Master | ||||||
Electives The entire course programs of ETH Zurich and the University of Zurich are open to the students to individual selection. | ||||||
Recommended Electives of Master Degree Programme | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0439-00L | Introduction 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". | W | 6 credits | 4G | K. W. Axhausen, R. Schubert | |
Abstract | The course presents basic economic principles as well as cost benefit analyses in transport; it also introduces methods used to derive the monetary values of non-market goods. | |||||
Objective | Familiarity with basic microeconomic and macroeconomic principles and with the essential methods of project appraisal | |||||
Content | Basic microeconomic and macroeconomic üpronciples; Cost-Benefit-Analyses; multi-criteria analyses; European guidelines; stated response methods; travel cost approach and others; Valuation of travel time savings; valuation of traffic safety | |||||
Lecture notes | moodle platform for the basic economic principles; handouts | |||||
Literature | Taylor, 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. | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
103-0255-AAL | Geodata Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 2 credits | 4R | M. Raubal | |
Abstract | The course deals with advanced methods in spatial data analysis. | |||||
Objective | - 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. | |||||
Content | The course deals with advanced methods in spatial data analysis in theory as well as in practical exercises. | |||||
Literature | MITCHELL, A., 2012, The Esri Guide to GIS Analysis - Modeling Suitability, Movement, and Interaction (3. Auflage), ESRI Press, Redlands, California | |||||
406-0242-AAL | Analysis II Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 7 credits | 15R | M. Akka Ginosar | |
Abstract | Mathematical tools of an engineer | |||||
Objective | Mathematics as a tool to solve engineering problems, mathematical formulation of problems in science and engineering. Basic mathematical knowledge of an engineers. | |||||
Content | 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. | |||||
Literature | Textbooks 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-AAL | Analysis I and II Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 14 credits | 30R | M. Akka Ginosar | |
Abstract | Mathematical tools for the engineer | |||||
Objective | Mathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers. | |||||
Content | Complex 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. | |||||
Literature | Textbooks 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 | |||||
Health Sciences and Technology Bachelor | ||||||
Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
551-1003-00L | Methods of Biological Analysis | W | 3 credits | 3G | R. Aebersold, M. Badertscher, K. Weis | |
Abstract | 529-1042-00 Principles of the most important separation techniques and the interpretation of molecular spectra. 551-1003-00 The course will teach the basis and typical applications of methods for the analysis of nucleic acid sequences, mass spectrometric analysis of proteins and proteomes and advanced light and fluorescent imaging methods. | |||||
Objective | 529-1042-00 Knowledge of the necessary basics and the possibilities of application of the relevant spectroscopical and separation methods in analytical chermistry. 551-1003-00 Knowledge of the theoretical basis of the methods for nucleic acid sequence analysis, mass spectrometry based protein and proteome analysis and advanced light and fluorescent imaging methods, and an understanding of the application of these principles in experimental biology. | |||||
Content | 529-1042-00 Application oriented basics of instrumental analysis in organic chemistry and the empirical employment of the methods of structure elucidation (mass spectrometry, NMR-, IR-, UV/VIS spectroscopy). Basics and application of chromatographic and electrophoretic separation methods. Application of the knowledge by practising. 551-1003-00 The course will consist of lectures covering the theoretical and technical base of the respective analytical methods and of exercises where typical applications of the methods in modern experimental biology are discussed. | |||||
Lecture notes | 529-1042-00 A comprehensive script is available in the HCI-Shop. A summary of the part "Spektroskopie" defines the relevant material for the exam. 551-1003-00 Materials supporting the lectures and exercises will be made available via Moodle. | |||||
Literature | 529-1042-00 - 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; | |||||
Prerequisites / Notice | 529-1042-00 Prerequisites: - 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.)" | |||||
Health Sciences and Technology Master | ||||||
Major in Human Movement Science and Sport | ||||||
Electives | ||||||
Elective Courses II | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
376-2019-00L | Applied Movement Analysis | W | 2 credits | 2G | R. Scharpf, S. Lorenzetti | |
Abstract | Based on practical examples out of sport, everyday movement and therapy, students use and compare different methods of movement analysis. | |||||
Objective | Students are able to assess human movement using different methods of movement analysis. | |||||
Content | During the course students get acquainted with different methods of movement analysis such as: functional, morphological, clinical, mechanical, and others. Based on practical examples, these methods are used and compared. The examples range from sport, everyday movement and therapy, such as hockey, gymnastics, acrobatics, badminton, gait / running and strength training. In the first phase of the class, the different approaches are applied. In the second phase, small teams are working on individual projects. These will be discussed and presented in plenum. | |||||
Lecture notes | Class material will be distributed using the moodle platform. | |||||
Major in Medical Technology | ||||||
Electives | ||||||
Elective Courses II | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0391-00L | Medical Image Analysis Does not take place this semester. | W | 3 credits | 2G | E. Konukoglu | |
Abstract | It 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. | |||||
Objective | This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas. | |||||
Prerequisites / Notice | Basic knowledge of computer vision would be helpful. | |||||
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
227-0969-00L | Methods & Models for fMRI Data Analysis | W | 6 credits | 4V | K. Stephan | |
Abstract | This 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. | |||||
Objective | To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their application to empirical fMRI data. | |||||
Content | This 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. | |||||
Major in Molecular Health Sciences | ||||||
Electives | ||||||
Elective Courses II | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
551-1003-00L | Methods of Biological Analysis | W | 3 credits | 3G | R. Aebersold, M. Badertscher, K. Weis | |
Abstract | 529-1042-00 Principles of the most important separation techniques and the interpretation of molecular spectra. 551-1003-00 The course will teach the basis and typical applications of methods for the analysis of nucleic acid sequences, mass spectrometric analysis of proteins and proteomes and advanced light and fluorescent imaging methods. | |||||
Objective | 529-1042-00 Knowledge of the necessary basics and the possibilities of application of the relevant spectroscopical and separation methods in analytical chermistry. 551-1003-00 Knowledge of the theoretical basis of the methods for nucleic acid sequence analysis, mass spectrometry based protein and proteome analysis and advanced light and fluorescent imaging methods, and an understanding of the application of these principles in experimental biology. | |||||
Content | 529-1042-00 Application oriented basics of instrumental analysis in organic chemistry and the empirical employment of the methods of structure elucidation (mass spectrometry, NMR-, IR-, UV/VIS spectroscopy). Basics and application of chromatographic and electrophoretic separation methods. Application of the knowledge by practising. 551-1003-00 The course will consist of lectures covering the theoretical and technical base of the respective analytical methods and of exercises where typical applications of the methods in modern experimental biology are discussed. | |||||
Lecture notes | 529-1042-00 A comprehensive script is available in the HCI-Shop. A summary of the part "Spektroskopie" defines the relevant material for the exam. 551-1003-00 Materials supporting the lectures and exercises will be made available via Moodle. | |||||
Literature | 529-1042-00 - 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; | |||||
Prerequisites / Notice | 529-1042-00 Prerequisites: - 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.)" | |||||
Major in Neurosciences | ||||||
Electives | ||||||
Elective Courses II | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
High-Energy Physics (Joint Master with EP Paris) | ||||||
Electives | ||||||
Optional Subjects in Mathematics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3461-00L | Functional Analysis I At most one of the three course units (Bachelor Core Courses) 401-3461-00L Functional Analysis I 401-3531-00L Differential Geometry I 401-3601-00L Probability Theory can be recognised for the Master's degree in Mathematics or Applied Mathematics. | W | 10 credits | 4V + 1U | A. Carlotto | |
Abstract | Baire 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. | |||||
Objective | Acquire 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. | |||||
Lecture notes | Lecture Notes on "Funktionalanalysis I" by Michael Struwe | |||||
Literature | A 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. | |||||
Prerequisites / Notice | Solid 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). | |||||
Computer Science Bachelor | ||||||
Bachelor Studies (Programme Regulations 2016) | ||||||
Basic Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0213-16L | Analysis II | O | 5 credits | 2V + 2U | Ö. Imamoglu | |
Abstract | Differential and Integral calculus in many variables, vector analysis. | |||||
Objective | Differential and Integral calculus in many variables, vector analysis. | |||||
Content | Differential and Integral calculus in many variables, vector analysis. | |||||
Literature | Für allgemeine Informationen, sehen Sie bitte die Webseite der Vorlesung: Link | |||||
Minor Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4271-00L | Data Analysis and Visualisation with Matlab in Earth Sciences | W | 3 credits | 3G | S. Wiemer, G. De Souza, T. Tormann | |
Abstract | This lecture and the corresponding exercises provide the students with an introduction to the concepts and tools of scientific data analysis. Based on current questions in the Earth Sciences, the students solve problems of increasing complexity both in small groups and singly using the software package MATLAB. Students also learn how to effectively visualise different kinds of datasets. | |||||
Objective | The following concepts are introduced in the course: - Effective data analysis and visualisation in 2D and 3D - Working with matrices and arrays - Programming and development of algorithms - Learning to effectively use animations - Statistical description of a dataset - Interactive data-mining - Uncertainty, error propagation and bootstrapping - Regression analysis - Testing hypotheses | |||||
Integrated Building Systems Master | ||||||
Main Courses | ||||||
Specialised Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0187-00L | Structural Reliability and Risk Analysis | W | 3 credits | 2G | S. Marelli | |
Abstract | Structural 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. | |||||
Objective | The 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. | |||||
Content | Engineers 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. | |||||
Lecture notes | Slides 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. | |||||
Literature | Ang, 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. | |||||
Prerequisites / Notice | Basic course on probability theory and statistics | |||||
Interdisciplinary Sciences Bachelor | ||||||
Physical-Chemical Direction | ||||||
1. Semester (Physical-Chemical Direction) | ||||||
Compulsory Subjects First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-1261-07L | Analysis I | O | 10 credits | 6V + 3U | M. Einsiedler | |
Abstract | Introduction to the differential and integral calculus in one real variable: fundaments of mathematical thinking, numbers, sequences, basic point set topology, continuity, differentiable functions, ordinary differential equations, Riemann integration. | |||||
Objective | The ability to work with the basics of calculus in a mathematically rigorous way. | |||||
Literature | H. Amann, J. Escher: Analysis I Link J. Appell: Analysis in Beispielen und Gegenbeispielen Link R. Courant: Vorlesungen über Differential- und Integralrechnung Link O. Forster: Analysis 1 Link H. Heuser: Lehrbuch der Analysis Link K. Königsberger: Analysis 1 Link W. Walter: Analysis 1 Link V. Zorich: Mathematical Analysis I (englisch) Link A. Beutelspacher: "Das ist o.B.d.A. trivial" Link H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten Link | |||||
3. Semester (Physical-Chemical Direction) | ||||||
Electives For the Bachelor in Interdisciplinary Sciences students can in principle choose from all subjects taught at the Bachelor level at ETH Zurich. At the beginning of the 2. year an individual study programme is established for every student in discussion with the Director of Studies in interdisciplinary sciences. For details see Programme Regulations 2010. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-2303-00L | Complex Analysis | W | 6 credits | 3V + 2U | R. Pandharipande | |
Abstract | Complex functions of one variable, Cauchy-Riemann equations, Cauchy theorem and integral formula, singularities, residue theorem, index of closed curves, analytic continuation, special functions, conformal mappings, Riemann mapping theorem. | |||||
Objective | Working Knowledge with functions of one complex variables; in particular applications of the residue theorem | |||||
Literature | Th. 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. R.Remmert: Theory of Complex Functions. Springer Verlag | |||||
Biochemical-Physical Direction | ||||||
1. Semester (Biochemical-Physical Direction) | ||||||
Compulsory Subjects First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0271-00L | Mathematical Foundations I: Analysis A | W | 5 credits | 3V + 2U | L. Kobel-Keller | |
Abstract | Introduction to calculus in one dimension. Building simple models and analysing them mathematically. Functions of one variable: the notion of a function, of the derivative, the idea of a differential equation, complex numbers, Taylor polynomials and Taylor series. The integral of a function of one variable. | |||||
Objective | Introduction to calculus in one dimension. Building simple models and analysing them mathematically. | |||||
Content | Functions of one variable: the notion of a function, of the derivative, the idea of a differential equation, complex numbers, Taylor polynomials and Taylor series. The integral of a function of one variable. | |||||
Literature | G. 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 further reading suggestions will be indicated during the lecture | |||||
401-1261-07L | Analysis I | W | 10 credits | 6V + 3U | M. Einsiedler | |
Abstract | Introduction to the differential and integral calculus in one real variable: fundaments of mathematical thinking, numbers, sequences, basic point set topology, continuity, differentiable functions, ordinary differential equations, Riemann integration. | |||||
Objective | The ability to work with the basics of calculus in a mathematically rigorous way. | |||||
Literature | H. Amann, J. Escher: Analysis I Link J. Appell: Analysis in Beispielen und Gegenbeispielen Link R. Courant: Vorlesungen über Differential- und Integralrechnung Link O. Forster: Analysis 1 Link H. Heuser: Lehrbuch der Analysis Link K. Königsberger: Analysis 1 Link W. Walter: Analysis 1 Link V. Zorich: Mathematical Analysis I (englisch) Link A. Beutelspacher: "Das ist o.B.d.A. trivial" Link H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten Link | |||||
401-0231-10L | Analysis I | W | 8 credits | 4V + 3U | T. H. Willwacher | |
Abstract | Calculus of one variable: Real and complex numbers, vectors, limits, sequences, series, power series, continuous maps, differentiation and integration in one variable, introduction to ordinary differential equations | |||||
Objective | Einfuehrung in die Grundlagen der Analysis | |||||
Lecture notes | Konrad Koenigsberger, Analysis I. Christian Blatter: Ingenieur-Analysis (Kapitel 1-3) | |||||
3. Semester (Biochemical-Physical Direction) | ||||||
Compulsory Subjects Examination Block | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0353-00L | Analysis III | W | 4 credits | 2V + 1U | A. Figalli | |
Abstract | In this lecture we treat problems in applied analysis. The focus lies on the simplest cases of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation and the wave equation. | |||||
Objective | ||||||
Content | 1.) 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) | |||||
Literature | Y. 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. Link | |||||
Prerequisites / Notice | Prerequisites: Analysis I and II, Fourier series (Komplexe Analysis) | |||||
Electives For the Bachelor in Interdisciplinary Sciences students can in principle choose from all subjects taught at the Bachelor level at ETH Zurich. At the beginning of the 2. year an individual study programme is established for every student in discussion with the Director of Studies in interdisciplinary sciences. For details see Programme Regulations 2010. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-2303-00L | Complex Analysis | W | 6 credits | 3V + 2U | R. Pandharipande | |
Abstract | Complex functions of one variable, Cauchy-Riemann equations, Cauchy theorem and integral formula, singularities, residue theorem, index of closed curves, analytic continuation, special functions, conformal mappings, Riemann mapping theorem. | |||||
Objective | Working Knowledge with functions of one complex variables; in particular applications of the residue theorem | |||||
Literature | Th. 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. R.Remmert: Theory of Complex Functions. Springer Verlag | |||||
Food Science Bachelor | ||||||
1. Semester | ||||||
First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0251-00L | Mathematics I | O | 6 credits | 4V + 2U | L. Halbeisen | |
Abstract | This course covers mathematical concepts and techniques necessary to model, solve and discuss scientific problems - notably through ordinary differential equations. | |||||
Objective | Mathematics is of ever increasing importance to the Natural Sciences and Engineering. The key is the so-called mathematical modelling cycle, i.e. the translation of problems from outside of mathematics into mathematics, the study of the mathematical problems (often with the help of high level mathematical software packages) and the interpretation of the results in the original environment. The goal of Mathematics I and II is to provide the mathematical foundations relevant for this paradigm. Differential equations are by far the most important tool for modelling and are therefore a main focus of both of these courses. | |||||
Content | 1. Single-Variable Calculus: review of differentiation, linearisation, Taylor polynomials, maxima and minima, antiderivative, fundamental theorem of calculus, integration methods, improper integrals. 2. Linear Algebra and Complex Numbers: systems of linear equations, Gauss-Jordan elimination, matrices, determinants, eigenvalues and eigenvectors, cartesian and polar forms for complex numbers, complex powers, complex roots, fundamental theorem of algebra. 3. Ordinary Differential Equations: separable ordinary differential equations (ODEs), integration by substitution, 1st and 2nd order linear ODEs, homogeneous systems of linear ODEs with constant coefficients, introduction to 2-dimensional dynamical systems. | |||||
Literature | - Thomas, G. B.: Thomas' Calculus, Part 1 (Pearson Addison-Wesley). - Bretscher, O.: Linear Algebra with Applications (Pearson Prentice Hall). | |||||
Prerequisites / Notice | Prerequisites: familiarity with the basic notions from Calculus, in particular those of function and derivative. Mathe-Lab (Assistance): Mondays 12-14, Tuesdays 17-19, Wednesdays 17-19, in Room HG E 41. | |||||
3. Semester | ||||||
Basic Courses II | ||||||
Examination Block 1 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-00L | Mathematics III: Systems Analysis | O | 4 credits | 2V + 1U | N. Gruber, M. Vogt | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
5. Semester | ||||||
Food Science General Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
752-1103-00L | Food Analysis II | W+ | 3 credits | 2V | T. Gude | |
Abstract | To get acquainted with the principles and applications of mass spectrometry in food analytics. | |||||
Objective | To get acquainted with the principles and applications of mass spectrometry in food analytics. | |||||
Content | Main focus: Mass spectrometry, applications of mass spectrometry (MS). | |||||
Lecture notes | The lectures are supplemented with handouts. | |||||
Electives (ONLY for Programme Regulations 2016) A list with possible electives will be published separately. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
551-1003-00L | Methods of Biological Analysis | W | 3 credits | 3G | R. Aebersold, M. Badertscher, K. Weis | |
Abstract | 529-1042-00 Principles of the most important separation techniques and the interpretation of molecular spectra. 551-1003-00 The course will teach the basis and typical applications of methods for the analysis of nucleic acid sequences, mass spectrometric analysis of proteins and proteomes and advanced light and fluorescent imaging methods. | |||||
Objective | 529-1042-00 Knowledge of the necessary basics and the possibilities of application of the relevant spectroscopical and separation methods in analytical chermistry. 551-1003-00 Knowledge of the theoretical basis of the methods for nucleic acid sequence analysis, mass spectrometry based protein and proteome analysis and advanced light and fluorescent imaging methods, and an understanding of the application of these principles in experimental biology. | |||||
Content | 529-1042-00 Application oriented basics of instrumental analysis in organic chemistry and the empirical employment of the methods of structure elucidation (mass spectrometry, NMR-, IR-, UV/VIS spectroscopy). Basics and application of chromatographic and electrophoretic separation methods. Application of the knowledge by practising. 551-1003-00 The course will consist of lectures covering the theoretical and technical base of the respective analytical methods and of exercises where typical applications of the methods in modern experimental biology are discussed. | |||||
Lecture notes | 529-1042-00 A comprehensive script is available in the HCI-Shop. A summary of the part "Spektroskopie" defines the relevant material for the exam. 551-1003-00 Materials supporting the lectures and exercises will be made available via Moodle. | |||||
Literature | 529-1042-00 - 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; | |||||
Prerequisites / Notice | 529-1042-00 Prerequisites: - 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.)" | |||||
Food Science Master | ||||||
Major in Food Processing | ||||||
Methodology Subjects | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W+ | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Major in Food Quality and Safety | ||||||
Methodology Subjects | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W+ | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Major in Nutrition and Health | ||||||
Methodology Subjects | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W+ | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Optional Subjects | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
766-6205-00L | Nutrient Analysis in Foods Number of participants limited to 15. Permission from lecturers required for all students. | W | 3 credits | 3U | M. B. Zimmermann, H. C. Winkler | |
Abstract | In 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. | |||||
Objective | Learning 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. | |||||
Content | The 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. | |||||
Lecture notes | A script and lecture slides are handed out before course start. | |||||
Prerequisites / Notice | Students 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. | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
752-1101-AAL | Food Analysis I Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 3 credits | 6R | L. Nyström | |
Abstract | To 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). | |||||
Objective | To 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). | |||||
Content | Fundamentals: 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). | |||||
Lecture notes | The lectures are supplemented with handouts. | |||||
Literature | Food Analysis - Fourth Edition, edited by S. Suzanne Nielson; 2010; Springer, Selected sections. | |||||
701-0071-AAL | Mathematics III: Systems Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | N. Gruber | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
MAS in Development and Cooperation The lectures and advanced training courses of NADEL are accessible only for students of the MAS in Development and Cooperation and for qualified employees with at least two years experience in development cooperation and a Master's level or equivalent level of education as recognized by ETH. PhD students doing empirical research in development cooperation may be admitted "sur Dossier". | ||||||
Advanced Training Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
865-0000-06L | Impact Analysis: Methods and Applications. Only for MAS/CAS in Development and Cooperation students, as well as specialists with at least 24 months of practical experience in international cooperation. Doctoral students dealing with empirical research in the area of development and cooperation (EZA) may be admitted "sur Dossier". Registration only through the NADEL administration office. | W | 2 credits | 3G | I. Günther | |
Abstract | The course gives an introduction to the most important methods for rigorous impact analysis of development programs and projects. The course is designed to both cover the most fundamental methods of impact analysis and introduce real world case studies from national, international and non-governmental development organizations and asks how rigorous impact analysis has influenced their policies. | |||||
Objective | Participants understand the most important methods of impact analysis. They are able to conduct small scale studies to evaluate the impact of their own programs as well as manage larger impact evaluations for their organizations. Participants are able to use the results of own and external impact studies. | |||||
Content | Introduction to rigorous impact analysis; Case studies and their policy implications; Introduction to the required statistical knowledge; Potentials and limitations of quantitative analysis; Experimental and quasi-experimental methods; Relevant and feasible indicators for the measurement of outcomes and impacts; Data collection and analysis; Project management of an impact analysis. | |||||
Prerequisites / Notice | Students of the course must fulfil requirements specified on the homepage of NADEL. Electronic registration may be done only after registration with NADEL secretariate. | |||||
MAS in Nutrition and Health | ||||||
Disciplinary Subjects | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
766-6205-00L | Nutrient Analysis in Foods Number of participants limited to 15. Permission from lecturers required for all students. | W+ | 3 credits | 3U | M. B. Zimmermann, H. C. Winkler | |
Abstract | In 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. | |||||
Objective | Learning 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. | |||||
Content | The 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. | |||||
Lecture notes | A script and lecture slides are handed out before course start. | |||||
Prerequisites / Notice | Students 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 Medical Physics | ||||||
Specialization: General Medical Physics and Biomedical Engineering | ||||||
Major in Bioimaging | ||||||
Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0391-00L | Medical Image Analysis Does not take place this semester. | W | 3 credits | 2G | E. Konukoglu | |
Abstract | It 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. | |||||
Objective | This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas. | |||||
Prerequisites / Notice | Basic knowledge of computer vision would be helpful. | |||||
227-0969-00L | Methods & Models for fMRI Data Analysis | W | 6 credits | 4V | K. Stephan | |
Abstract | This 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. | |||||
Objective | To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their application to empirical fMRI data. | |||||
Content | This 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 | ||||||
Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
860-0002-00L | Quantitative Policy Analysis and Modeling | O | 6 credits | 4G | A. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet | |
Abstract | The 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 | |||||
Objective | The 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 The Master of Advanced Studies in Sustainable Water Resources is a 12 month full time postgraduate diploma programme. The focus of the programme is on issues of sustainability and water resources in Latin America, with special attention given to the impacts of development and climate change on water resources. The programme combines multidisciplinary coursework with high level research. Sample research topics include: water quality, water quantity, water for agriculture, water for the environment, adaptation to climate change, and integrated water resource management. Language: English. Credit hours: 66 ECTS. For further information please visit: Link | ||||||
Core Courses Foundation courses: 12 credits have to be achieved. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0227-00L | Systems Analysis and Mathematical Modeling in Urban Water Management | W | 6 credits | 4G | E. Morgenroth, M. Maurer | |
Abstract | Systematic 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. | |||||
Objective | The 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. | |||||
Content | The 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) | |||||
Lecture notes | Copies of overheads will be made available. | |||||
Literature | There will be a required textbook that students need to purchase: Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg | |||||
Prerequisites / Notice | This course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously. | |||||
Electives Electives: 6 credits has to be achieved. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
Mechanical Engineering Bachelor | ||||||
1. Semester Registration for the exercises via the application Link with your nETHz login (username, password). | ||||||
First Year Examinations: Compulsory Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0261-G0L | Analysis I | O | 8 credits | 5V + 3U | A. Steiger | |
Abstract | Differential and integral calculus for functions of one and several variables; vector analysis; ordinary differential equations of first and of higher order, systems of ordinary differential equations; power series. The mathematical methods are applied in a large number of examples from mechanics, physics and other areas which are basic to engineering. | |||||
Objective | Introduction to the mathematical foundations of engineering sciences, as far as concerning differential and integral calculus. | |||||
Literature | U. Stammbach: Analysis I/II | |||||
Prerequisites / Notice | Die 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 | ||||||
Compulsory Courses | ||||||
Examination Block 1 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0363-10L | Analysis III | O | 3 credits | 2V + 1U | F. Da Lio | |
Abstract | Introduction 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. | |||||
Objective | Mathematical 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 Link | |||||
Content | Laplace 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 | |||||
Lecture notes | Lecture notes by Prof. Dr. Alessandra Iozzi: Link | |||||
Literature | E. 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 Link | |||||
5. Semester | ||||||
Focus Specialization | ||||||
Design, Mechanics and Materials Focus Coordinator: Prof. Kristina Shea In order to achieve the required 20 credit points for the Focus Specialization Design, Mechanics and Material you are free to choose any of the courses offered within the focus and are encouraged to select among those recommended. If you wish to take one of the Master level courses, you must get approval from the lecturer. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0360-00L | Procedures for the Analysis of Structures | W+ | 4 credits | 2V + 1U | G. Kress | |
Abstract | Basic theories for structure integrity calculations are presented with focus on strength, stability, fatigue and elasto-plastic structural analysis. Theories and models for one dimesional and planar structures are presented based on energy theorems. | |||||
Objective | Basic principles applied in structural mechanics. Introduction to the theories of planar structures. Development of an understanding of the relationship between material properties, structural theories and design criteria. | |||||
Content | 1. Basic problem of continuum mechanics and energy principles: structural theories, homogenization theories; finite elements; fracture mechanics. 2.Structural theories for planar structures and stability: plane-stress, plate theory, buckling of plates (non-linear plate theory). 3.Strength of material theories and material properties: ductile behaviour, plasticity, von Mises, Tresca, principal stress criterion; brittle behaviour; viscoplastic behaviour, creep resistance. 4. Structural design: fatigue and dynamic structural analysis. | |||||
Lecture notes | Script and all other course material available on MOODLE | |||||
Prerequisites / Notice | none | |||||
Engineering Tools IV The participation at the Engineering Tools course is mandatory. If you miss any classes, no credit points will be awarded. For exemptions you have to contact the lecturer of the course. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0015-10L | Engineering Tool IV: Experimental Modal Analysis All Engineering Tool courses are for MAVT-Bachelor students only. Number of participants limited to 16. Only one course can be chosen per semester. | W | 0.4 credits | 1K | F. Kuster | |
Abstract | Measuring- and analysis-methods for the determination of transfer functions of mechanical structures. Evaluation and preparation of the measured data for visualisation and interpretation of the dynamic behaviour. | |||||
Objective | Introduction into the practical application of measuring- and analysis-methods for determination of transfer functions of mechanical structures. Evaluation and preparation of the measured data for visualisation and interpretation of the dynamic behaviour. | |||||
Content | Acquaintance with the acceleration- and force-sensors, measurement of transfer functions of mechanical structures, determination and description of modes of vibration by means of practical examples, introduction into the vibration theory and its fundamental terms, discrete oscillator. | |||||
Lecture notes | yes, distribution in the course (CHF 20.-) | |||||
Literature | David Ewins, Modal Testing: Theory and Practice | |||||
Prerequisites / Notice | In the practical part of the course the participants self will make measurements on structures and then analyse them for eigenfrequencies and modes of vibrations. | |||||
Mechanical Engineering Master | ||||||
Core Courses | ||||||
Energy, Flows and Processes The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0187-00L | Structural Reliability and Risk Analysis | W | 3 credits | 2G | S. Marelli | |
Abstract | Structural 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. | |||||
Objective | The 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. | |||||
Content | Engineers 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. | |||||
Lecture notes | Slides 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. | |||||
Literature | Ang, 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. | |||||
Prerequisites / Notice | Basic course on probability theory and statistics | |||||
Mechanics, Materials, Structures The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0360-00L | Procedures for the Analysis of Structures | W | 4 credits | 2V + 1U | G. Kress | |
Abstract | Basic theories for structure integrity calculations are presented with focus on strength, stability, fatigue and elasto-plastic structural analysis. Theories and models for one dimesional and planar structures are presented based on energy theorems. | |||||
Objective | Basic principles applied in structural mechanics. Introduction to the theories of planar structures. Development of an understanding of the relationship between material properties, structural theories and design criteria. | |||||
Content | 1. Basic problem of continuum mechanics and energy principles: structural theories, homogenization theories; finite elements; fracture mechanics. 2.Structural theories for planar structures and stability: plane-stress, plate theory, buckling of plates (non-linear plate theory). 3.Strength of material theories and material properties: ductile behaviour, plasticity, von Mises, Tresca, principal stress criterion; brittle behaviour; viscoplastic behaviour, creep resistance. 4. Structural design: fatigue and dynamic structural analysis. | |||||
Lecture notes | Script and all other course material available on MOODLE | |||||
Prerequisites / Notice | none | |||||
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: 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 The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Micro & Nanosystems The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0377-00L | Physics of Failure and Failure Analysis of Electronic Devices and Equipment | W | 3 credits | 2V | U. Sennhauser | |
Abstract | Failures 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. | |||||
Objective | Introduction 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. | |||||
Content | Summary 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 | |||||
Lecture notes | Comprehensive copy of transparencies | |||||
Bioengineering The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
406-0353-AAL | Analysis III Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | F. Da Lio | |
Abstract | Introduction 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. | |||||
Objective | Mathematical treatment of problems in science and engineering. To understand the properties of the different types of partlial differentail equations. | |||||
Content | Laplace 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 | E. 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) | |||||
Prerequisites / Notice | Up-to-date information about this course can be found at: Link | |||||
Materials Science Bachelor | ||||||
1. Semester | ||||||
Basis Courses Part 1 | ||||||
First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0261-GUL | Analysis I | O | 8 credits | 5V + 3U | A. Steiger | |
Abstract | Differential and integral calculus for functions of one and several variables; vector analysis; ordinary differential equations of first and of higher order, systems of ordinary differential equations; power series. The mathematical methods are applied in a large number of examples from mechanics, physics and other areas which are basic to engineering. | |||||
Objective | Introduction to the mathematical foundations of engineering sciences, as far as concerning differential and integral calculus. | |||||
Literature | U. Stammbach: Analysis I/II | |||||
Prerequisites / Notice | Die 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 | ||||||
Basic Courses Part 2 | ||||||
Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0363-10L | Analysis III | O | 3 credits | 2V + 1U | F. Da Lio | |
Abstract | Introduction 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. | |||||
Objective | Mathematical 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 Link | |||||
Content | Laplace 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 | |||||
Lecture notes | Lecture notes by Prof. Dr. Alessandra Iozzi: Link | |||||
Literature | E. 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 Link | |||||
Mathematics Bachelor | ||||||
First Year Compulsory Courses | ||||||
First Year Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-1261-07L | Analysis I | O | 10 credits | 6V + 3U | M. Einsiedler | |
Abstract | Introduction to the differential and integral calculus in one real variable: fundaments of mathematical thinking, numbers, sequences, basic point set topology, continuity, differentiable functions, ordinary differential equations, Riemann integration. | |||||
Objective | The ability to work with the basics of calculus in a mathematically rigorous way. | |||||
Literature | H. Amann, J. Escher: Analysis I Link J. Appell: Analysis in Beispielen und Gegenbeispielen Link R. Courant: Vorlesungen über Differential- und Integralrechnung Link O. Forster: Analysis 1 Link H. Heuser: Lehrbuch der Analysis Link K. Königsberger: Analysis 1 Link W. Walter: Analysis 1 Link V. Zorich: Mathematical Analysis I (englisch) Link A. Beutelspacher: "Das ist o.B.d.A. trivial" Link H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten Link | |||||
Compulsory Courses | ||||||
Examination Block I In Examination Block I either the course unit 402-2883-00L Physics III or the course unit 402-2203-01L Classical Mechanics must be chosen and registered for an examination. (Students may also enrol for the other of the two course units; within the ETH Bachelor's programme in mathematics, this other course unit cannot be registered in myStudies for an examination nor can it be recognised for the Bachelor's degree.) | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-2303-00L | Complex Analysis | O | 6 credits | 3V + 2U | R. Pandharipande | |
Abstract | Complex functions of one variable, Cauchy-Riemann equations, Cauchy theorem and integral formula, singularities, residue theorem, index of closed curves, analytic continuation, special functions, conformal mappings, Riemann mapping theorem. | |||||
Objective | Working Knowledge with functions of one complex variables; in particular applications of the residue theorem | |||||
Literature | Th. 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. R.Remmert: Theory of Complex Functions. Springer Verlag | |||||
Core Courses | ||||||
Core Courses: Pure Mathematics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3461-00L | Functional Analysis I At most one of the three course units (Bachelor Core Courses) 401-3461-00L Functional Analysis I 401-3531-00L Differential Geometry I 401-3601-00L Probability Theory can be recognised for the Master's degree in Mathematics or Applied Mathematics. | W | 10 credits | 4V + 1U | A. Carlotto | |
Abstract | Baire 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. | |||||
Objective | Acquire 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. | |||||
Lecture notes | Lecture Notes on "Funktionalanalysis I" by Michael Struwe | |||||
Literature | A 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. | |||||
Prerequisites / Notice | Solid 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). | |||||
Electives | ||||||
Selection: Probability Theory, Statistics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4623-00L | Time Series Analysis Does not take place this semester. | W | 6 credits | 3G | not available | |
Abstract | Statistical 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. | |||||
Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Content | This 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. | |||||
Lecture notes | Not available | |||||
Literature | A list of references will be distributed during the course. | |||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Seminars Early enrolments for seminars in myStudies are encouraged, so that we will recognize need for additional seminars in a timely manner. Some seminars have waiting lists. Nevertheless, register for at most two mathematics seminars. In this case, you express a stronger preference for the seminar for which you register earlier. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3680-67L | Persistent Homology and Topological Data Analysis Number of participants limited to 8. | W | 4 credits | 2S | P. S. Jossen | |
Abstract | We 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). | |||||
Objective | To get familiar with the basic concepts of topological data analysis and see some applications thereof. | |||||
Literature | Herbert Edelsbrunner and John L. Harer: Computational Topology, An Introduction. AMS 2010 | |||||
Prerequisites / Notice | Participants are supposed to be familiar with singular homology. | |||||
401-3650-67L | Numerical Analysis Seminar: Tensor Numerics and Deep Neural Networks Number of participants limited to 10. | W | 4 credits | 2S | C. Schwab | |
Abstract | The 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. | |||||
Objective | The 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 Link Numerical experiments will be done with TENSORFLOW and with the TT toolbox at Link | |||||
Lecture notes | The seminar will study a set of 10 orginial papers from 2015 to today. | |||||
Literature | Helmut 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. | |||||
Prerequisites / Notice | Completed BSc MATH exam. | |||||
Mathematics Master | ||||||
Core Courses For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 15 of the required 28 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields. | ||||||
Bachelor Core Courses: Pure Mathematics Further restrictions apply, but in particular: 401-3531-00L Differential Geometry I can only be recognised for the Master Programme if 401-3532-00L Differential Geometry II has not been recognised for the Bachelor Programme. Analogously for: 401-3461-00L Functional Analysis I - 401-3462-00L Functional Analysis II 401-3001-61L Algebraic Topology I - 401-3002-12L Algebraic Topology II 401-3132-00L Commutative Algebra - 401-3146-12L Algebraic Geometry 401-3371-00L Dynamical Systems I - 401-3372-00L Dynamical Systems II For the category assignment take contact with the Study Administration Office (Link) after having received the credits. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3461-00L | Functional Analysis I At most one of the three course units (Bachelor Core Courses) 401-3461-00L Functional Analysis I 401-3531-00L Differential Geometry I 401-3601-00L Probability Theory can be recognised for the Master's degree in Mathematics or Applied Mathematics. | E- | 10 credits | 4V + 1U | A. Carlotto | |
Abstract | Baire 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. | |||||
Objective | Acquire 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. | |||||
Lecture notes | Lecture Notes on "Funktionalanalysis I" by Michael Struwe | |||||
Literature | A 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. | |||||
Prerequisites / Notice | Solid 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). | |||||
Electives For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 15 of the required 28 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields. | ||||||
Electives: Applied Mathematics and Further Application-Oriented Fields ¬ | ||||||
Selection: Numerical Analysis | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4657-00L | Numerical Analysis of Stochastic Ordinary Differential Equations Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods" | W | 6 credits | 3V + 1U | A. Jentzen | |
Abstract | Course 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. | |||||
Objective | The 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. | |||||
Content | Generation 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 | |||||
Lecture notes | Lecture Notes are available in the lecture homepage (please follow the link in the Learning materials section). | |||||
Literature | P. 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. | |||||
Prerequisites / Notice | Prerequisites: 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. | |||||
Selection: Probability Theory, Statistics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
401-4623-00L | Time Series Analysis Does not take place this semester. | W | 6 credits | 3G | not available | |
Abstract | Statistical 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. | |||||
Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Content | This 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. | |||||
Lecture notes | Not available | |||||
Literature | A list of references will be distributed during the course. | |||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||
Application Area Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area. | ||||||
Image Processing and Computer Vision | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
Seminars and Semester Papers | ||||||
Seminars Early enrolments for seminars in myStudies are encouraged, so that we will recognize need for additional seminars in a timely manner. Some seminars have waiting lists. Nevertheless, register for at most two mathematics seminars. In this case, you express a stronger preference for the seminar for which you register earlier. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3680-67L | Persistent Homology and Topological Data Analysis Number of participants limited to 8. | W | 4 credits | 2S | P. S. Jossen | |
Abstract | We 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). | |||||
Objective | To get familiar with the basic concepts of topological data analysis and see some applications thereof. | |||||
Literature | Herbert Edelsbrunner and John L. Harer: Computational Topology, An Introduction. AMS 2010 | |||||
Prerequisites / Notice | Participants are supposed to be familiar with singular homology. | |||||
401-3650-67L | Numerical Analysis Seminar: Tensor Numerics and Deep Neural Networks Number of participants limited to 10. | W | 4 credits | 2S | C. Schwab | |
Abstract | The 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. | |||||
Objective | The 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 Link Numerical experiments will be done with TENSORFLOW and with the TT toolbox at Link | |||||
Lecture notes | The seminar will study a set of 10 orginial papers from 2015 to today. | |||||
Literature | Helmut 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. | |||||
Prerequisites / Notice | Completed BSc MATH exam. | |||||
Additional Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-5350-00L | Analysis Seminar | E- | 0 credits | 1K | M. Struwe, A. Carlotto, F. Da Lio, A. Figalli, N. Hungerbühler, T. Ilmanen, T. Kappeler, T. Rivière, D. A. Salamon | |
Abstract | Research colloquium | |||||
Objective | ||||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
406-2303-AAL | Complex Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 6 credits | 13R | R. Pandharipande | |
Abstract | Complex 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. | |||||
Objective | ||||||
Literature | 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. R.Remmert: Theory of Complex Functions.. Springer Verlag E.Hille: Analytic Function Theory. AMS Chelsea Publication | |||||
406-3461-AAL | Functional Analysis I Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 10 credits | 21R | A. Carlotto | |
Abstract | Baire 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. | |||||
Objective | Acquire 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. | |||||
Lecture notes | Lecture Notes on "Funktionalanalysis I" by Michael Struwe. | |||||
Literature | A 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. | |||||
Micro- and Nanosystems Master | ||||||
Core Courses | ||||||
Elective Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0377-00L | Physics of Failure and Failure Analysis of Electronic Devices and Equipment | W | 3 credits | 2V | U. Sennhauser | |
Abstract | Failures 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. | |||||
Objective | Introduction 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. | |||||
Content | Summary 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 | |||||
Lecture notes | Comprehensive copy of transparencies | |||||
Neural Systems and Computation Master | ||||||
Core Courses | ||||||
Compulsory Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-1039-00L | Basics of Instrumentation, Measurement, and Analysis (University of Zurich) 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: Link 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. | O | 4 credits | 9S | S.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, W. von der Behrens | |
Abstract | Experimental 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). | |||||
Objective | The 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. | |||||
Prerequisites / Notice | For 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. | |||||
Physics Bachelor | ||||||
First Year Compulsory Courses | ||||||
First Year Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-1261-07L | Analysis I | O | 10 credits | 6V + 3U | M. Einsiedler | |
Abstract | Introduction to the differential and integral calculus in one real variable: fundaments of mathematical thinking, numbers, sequences, basic point set topology, continuity, differentiable functions, ordinary differential equations, Riemann integration. | |||||
Objective | The ability to work with the basics of calculus in a mathematically rigorous way. | |||||
Literature | H. Amann, J. Escher: Analysis I Link J. Appell: Analysis in Beispielen und Gegenbeispielen Link R. Courant: Vorlesungen über Differential- und Integralrechnung Link O. Forster: Analysis 1 Link H. Heuser: Lehrbuch der Analysis Link K. Königsberger: Analysis 1 Link W. Walter: Analysis 1 Link V. Zorich: Mathematical Analysis I (englisch) Link A. Beutelspacher: "Das ist o.B.d.A. trivial" Link H. Schichl, R. Steinbauer: Einführung in das mathematische Arbeiten Link | |||||
Compulsory Courses | ||||||
Second Year Compulsory Courses | ||||||
Examination Block I | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-2303-00L | Complex Analysis | O | 6 credits | 3V + 2U | R. Pandharipande | |
Abstract | Complex functions of one variable, Cauchy-Riemann equations, Cauchy theorem and integral formula, singularities, residue theorem, index of closed curves, analytic continuation, special functions, conformal mappings, Riemann mapping theorem. | |||||
Objective | Working Knowledge with functions of one complex variables; in particular applications of the residue theorem | |||||
Literature | Th. 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. R.Remmert: Theory of Complex Functions. Springer Verlag | |||||
Selection of Higher Semester Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3461-00L | Functional Analysis I At most one of the three course units (Bachelor Core Courses) 401-3461-00L Functional Analysis I 401-3531-00L Differential Geometry I 401-3601-00L Probability Theory can be recognised for the Master's degree in Mathematics or Applied Mathematics. | W | 10 credits | 4V + 1U | A. Carlotto | |
Abstract | Baire 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. | |||||
Objective | Acquire 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. | |||||
Lecture notes | Lecture Notes on "Funktionalanalysis I" by Michael Struwe | |||||
Literature | A 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. | |||||
Prerequisites / Notice | Solid 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). | |||||
Physics Master | ||||||
Electives | ||||||
Electives: Physics and Mathematics | ||||||
Selection: Mathematics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-3461-00L | Functional Analysis I At most one of the three course units (Bachelor Core Courses) 401-3461-00L Functional Analysis I 401-3531-00L Differential Geometry I 401-3601-00L Probability Theory can be recognised for the Master's degree in Mathematics or Applied Mathematics. | W | 10 credits | 4V + 1U | A. Carlotto | |
Abstract | Baire 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. | |||||
Objective | Acquire 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. | |||||
Lecture notes | Lecture Notes on "Funktionalanalysis I" by Michael Struwe | |||||
Literature | A 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. | |||||
Prerequisites / Notice | Solid 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 see Link Students in the Joint Degree Master's Programme "Quantitative Finance" must book University of Zurich modules directly at the University of Zurich. Those modules are not listed here. | ||||||
Elective Courses | ||||||
Mathematical Methods for Finance For possible additional course offerings see Link | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4657-00L | Numerical Analysis of Stochastic Ordinary Differential Equations Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods" | W | 6 credits | 3V + 1U | A. Jentzen | |
Abstract | Course 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. | |||||
Objective | The 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. | |||||
Content | Generation 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 | |||||
Lecture notes | Lecture Notes are available in the lecture homepage (please follow the link in the Learning materials section). | |||||
Literature | P. 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. | |||||
Prerequisites / Notice | Prerequisites: 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. | |||||
Spatial Development and Infrastructure Systems Master | ||||||
1. Semester | ||||||
Major Courses | ||||||
Major in Spatial and Landscape Development | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
103-0307-00L | Multi-Criteria Decision Analysis | W | 3 credits | 2G | A. Grêt-Regamey | |
Abstract | Planners need to make decisions about the best possible mix of land uses. With increasing availability of spatial databases and the analytical capabilities of GIS, more effective decision support systems can be developed. The goal of the course is to provide the basics of spatial analysis and to teach the integration of spatial data into multicriteria decision-making systems. | |||||
Objective | This course will: 1) introduce students to techniques and issues associated with spatial modeling and decision support systems, including analytical techniques that are unique to spatial analysis 2) provide hands-on training in the use of these spatial tools in R while addressing real planning problems. The emphasis is on concepts, resources, and analysis tools that students can use in science, private companies and government careers. | |||||
Lecture notes | - Handouts of the lectures - Script - Exercise material Download: Link | |||||
Prerequisites / Notice | The course will presume basic knowledge of the R software package. RE&IS Master students will acquire this knowledge during the "Basics of RE&IS" (103-0377-10L) course. Provided there are still available places, students from other disciplines can also join the part of "Basics of RE&IS" in which R is taught (i.e. first five lectures; no credit points will be awarded). These students can register for "Basics of RE&IS" by e-mailing Maarten van Strien (Link). Alternatively, they can acquire basic R knowledge with online tutorials, such as "Introduction to R" by W. N. Venables and D. M. Smith available online at Link. | |||||
Network Infrastructure | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0187-00L | Structural Reliability and Risk Analysis | W | 3 credits | 2G | S. Marelli | |
Abstract | Structural 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. | |||||
Objective | The 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. | |||||
Content | Engineers 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. | |||||
Lecture notes | Slides 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. | |||||
Literature | Ang, 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. | |||||
Prerequisites / Notice | Basic course on probability theory and statistics | |||||
103-0307-00L | Multi-Criteria Decision Analysis | W | 3 credits | 2G | A. Grêt-Regamey | |
Abstract | Planners need to make decisions about the best possible mix of land uses. With increasing availability of spatial databases and the analytical capabilities of GIS, more effective decision support systems can be developed. The goal of the course is to provide the basics of spatial analysis and to teach the integration of spatial data into multicriteria decision-making systems. | |||||
Objective | This course will: 1) introduce students to techniques and issues associated with spatial modeling and decision support systems, including analytical techniques that are unique to spatial analysis 2) provide hands-on training in the use of these spatial tools in R while addressing real planning problems. The emphasis is on concepts, resources, and analysis tools that students can use in science, private companies and government careers. | |||||
Lecture notes | - Handouts of the lectures - Script - Exercise material Download: Link | |||||
Prerequisites / Notice | The course will presume basic knowledge of the R software package. RE&IS Master students will acquire this knowledge during the "Basics of RE&IS" (103-0377-10L) course. Provided there are still available places, students from other disciplines can also join the part of "Basics of RE&IS" in which R is taught (i.e. first five lectures; no credit points will be awarded). These students can register for "Basics of RE&IS" by e-mailing Maarten van Strien (Link). Alternatively, they can acquire basic R knowledge with online tutorials, such as "Introduction to R" by W. N. Venables and D. M. Smith available online at Link. | |||||
Major Courses for all Majors | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0439-00L | Introduction 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". | W | 6 credits | 4G | K. W. Axhausen, R. Schubert | |
Abstract | The course presents basic economic principles as well as cost benefit analyses in transport; it also introduces methods used to derive the monetary values of non-market goods. | |||||
Objective | Familiarity with basic microeconomic and macroeconomic principles and with the essential methods of project appraisal | |||||
Content | Basic microeconomic and macroeconomic üpronciples; Cost-Benefit-Analyses; multi-criteria analyses; European guidelines; stated response methods; travel cost approach and others; Valuation of travel time savings; valuation of traffic safety | |||||
Lecture notes | moodle platform for the basic economic principles; handouts | |||||
Literature | Taylor, 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. | |||||
Electives The entire course programs of ETH Zurich and Universitiy Zurich are open to the students to individual selection. The students have themselves to check whether they meet the admission requirements for a course. | ||||||
Recommended Electives of Master Degree Programme Students having enroled for 851-0703-03 earlier (i.e. bachelor's degree programme or as additional requirement for master's degree programme) cannot enrol for this again during master's degree programme. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
406-0242-AAL | Analysis II Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 7 credits | 15R | M. Akka Ginosar | |
Abstract | Mathematical tools of an engineer | |||||
Objective | Mathematics as a tool to solve engineering problems, mathematical formulation of problems in science and engineering. Basic mathematical knowledge of an engineers. | |||||
Content | 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. | |||||
Literature | Textbooks 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 | |||||
Computational Science and Engineering Bachelor | ||||||
First Year Compulsory Courses | ||||||
First Year Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0231-10L | Analysis I | O | 8 credits | 4V + 3U | T. H. Willwacher | |
Abstract | Calculus of one variable: Real and complex numbers, vectors, limits, sequences, series, power series, continuous maps, differentiation and integration in one variable, introduction to ordinary differential equations | |||||
Objective | Einfuehrung in die Grundlagen der Analysis | |||||
Lecture notes | Konrad Koenigsberger, Analysis I. Christian Blatter: Ingenieur-Analysis (Kapitel 1-3) | |||||
Basic Courses | ||||||
Block G1 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0353-00L | Analysis III | O | 4 credits | 2V + 1U | A. Figalli | |
Abstract | In this lecture we treat problems in applied analysis. The focus lies on the simplest cases of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation and the wave equation. | |||||
Objective | ||||||
Content | 1.) 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) | |||||
Literature | Y. 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. Link | |||||
Prerequisites / Notice | Prerequisites: Analysis I and II, Fourier series (Komplexe Analysis) | |||||
Fields of Specialization | ||||||
Computational Finance | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4657-00L | Numerical Analysis of Stochastic Ordinary Differential Equations Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods" | W | 6 credits | 3V + 1U | A. Jentzen | |
Abstract | Course 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. | |||||
Objective | The 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. | |||||
Content | Generation 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 | |||||
Lecture notes | Lecture Notes are available in the lecture homepage (please follow the link in the Learning materials section). | |||||
Literature | P. 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. | |||||
Prerequisites / Notice | Prerequisites: 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. | |||||
Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
401-4623-00L | Time Series Analysis Does not take place this semester. | W | 6 credits | 3G | not available | |
Abstract | Statistical 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. | |||||
Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Content | This 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. | |||||
Lecture notes | Not available | |||||
Literature | A list of references will be distributed during the course. | |||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||
Computational Science and Engineering Master | ||||||
Fields of Specialization | ||||||
Computational Finance | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4657-00L | Numerical Analysis of Stochastic Ordinary Differential Equations Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods" | W | 6 credits | 3V + 1U | A. Jentzen | |
Abstract | Course 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. | |||||
Objective | The 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. | |||||
Content | Generation 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 | |||||
Lecture notes | Lecture Notes are available in the lecture homepage (please follow the link in the Learning materials section). | |||||
Literature | P. 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. | |||||
Prerequisites / Notice | Prerequisites: 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. | |||||
Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
401-4623-00L | Time Series Analysis Does not take place this semester. | W | 6 credits | 3G | not available | |
Abstract | Statistical 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. | |||||
Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Content | This 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. | |||||
Lecture notes | Not available | |||||
Literature | A list of references will be distributed during the course. | |||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
406-0353-AAL | Analysis III Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | F. Da Lio | |
Abstract | Introduction 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. | |||||
Objective | Mathematical treatment of problems in science and engineering. To understand the properties of the different types of partlial differentail equations. | |||||
Content | Laplace 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 | E. 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) | |||||
Prerequisites / Notice | Up-to-date information about this course can be found at: Link | |||||
Robotics, Systems and Control Master | ||||||
Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Abstract | Light 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. | |||||
Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Content | The 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. | |||||
Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C. The course language is English. | |||||
227-0526-00L | Power System Analysis | W | 6 credits | 4G | G. Hug | |
Abstract | The goal of this course is understanding the stationary and dynamic problems in electrical power systems. The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power networks. | |||||
Objective | The goal of this course is understanding the stationary and dynamic problems in electrical power systems and the application of analysis tools in steady and dynamic states. | |||||
Content | The course includes the development of stationary models of the electrical network, their mathematical representation and special characteristics and solution methods of large linear and non-linear systems of equations related to electrical power grids. Approaches such as the Newton-Raphson algorithm applied to power flow equations, superposition technique for short-circuit analysis, equal area criterion and nose curve analysis are discussed as well as power flow computation techniques for distribution grids. | |||||
Lecture notes | Lecture notes. | |||||
Science, Technology, and Policy Master | ||||||
Core Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
860-0002-00L | Quantitative Policy Analysis and Modeling | O | 6 credits | 4G | A. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet | |
Abstract | The 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 | |||||
Objective | The 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. | |||||
Electives | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
101-0439-00L | Introduction 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". | W | 6 credits | 4G | K. W. Axhausen, R. Schubert | |
Abstract | The course presents basic economic principles as well as cost benefit analyses in transport; it also introduces methods used to derive the monetary values of non-market goods. | |||||
Objective | Familiarity with basic microeconomic and macroeconomic principles and with the essential methods of project appraisal | |||||
Content | Basic microeconomic and macroeconomic üpronciples; Cost-Benefit-Analyses; multi-criteria analyses; European guidelines; stated response methods; travel cost approach and others; Valuation of travel time savings; valuation of traffic safety | |||||
Lecture notes | moodle platform for the basic economic principles; handouts | |||||
Literature | Taylor, 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 Teaching Diploma Detailed information on the programme at: Link | ||||||
Additional Requirements in Sports Science | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
376-2019-00L | Applied Movement Analysis | W | 2 credits | 2G | R. Scharpf, S. Lorenzetti | |
Abstract | Based on practical examples out of sport, everyday movement and therapy, students use and compare different methods of movement analysis. | |||||
Objective | Students are able to assess human movement using different methods of movement analysis. | |||||
Content | During the course students get acquainted with different methods of movement analysis such as: functional, morphological, clinical, mechanical, and others. Based on practical examples, these methods are used and compared. The examples range from sport, everyday movement and therapy, such as hockey, gymnastics, acrobatics, badminton, gait / running and strength training. In the first phase of the class, the different approaches are applied. In the second phase, small teams are working on individual projects. These will be discussed and presented in plenum. | |||||
Lecture notes | Class material will be distributed using the moodle platform. | |||||
Statistics Master The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible. | ||||||
Core Courses In each subject area, the core courses offered are normally mathematical as well as application-oriented in content. For each subject area, only one of these is recognised for the Master degree. | ||||||
Analysis of Variance and Design of Experiments | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
Time Series and Stochastic Processes | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-4623-00L | Time Series Analysis Does not take place this semester. | W | 6 credits | 3G | not available | |
Abstract | Statistical 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. | |||||
Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Content | This 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. | |||||
Lecture notes | Not available | |||||
Literature | A list of references will be distributed during the course. | |||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||
Specialization Areas and Electives | ||||||
Statistical and Mathematical Courses | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R efficiently for data analysis. | |||||
Content | The 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: Link | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | Basic 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 Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: Link | W | 5 credits | 3G | H. Rehrauer, M. Robinson | |
Abstract | A 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. | |||||
Objective | -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 | |||||
Content | Lectures 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 | |||||
Lecture notes | Lecture notes, published manuscripts | |||||
Prerequisites / Notice | Prerequisites: 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 | |||||
Statistical and Mathematical Courses: not eligible for credits | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | E- | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
Environmental Engineering Bachelor | ||||||
1. Semester | ||||||
First Year Examinations (1. Sem.) | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0241-00L | Analysis I | O | 7 credits | 5V + 2U | M. Akka Ginosar | |
Abstract | Mathematical tools for the engineer | |||||
Objective | Mathematics as a tool to solve engineering problems. Mathematical formulation of technical and scientific problems. Basic mathematical knowledge for engineers. | |||||
Content | Complex numbers. Calculus for functions of one variable with applications. Simple Mathematical models in engineering. | |||||
Lecture notes | Die Vorlesung folgt weitgehend Klaus Dürrschnabel, "Mathematik für Ingenieure - Eine Einführung mit Anwendungs- und Alltagsbeispielen", Springer; online verfügbar unter: Link | |||||
Literature | Neben 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: Link Meike Akveld, "Analysis 1", vdf; Link Urs Stammbach, "Analysis I/II" (erhältlich im ETH Store); Link | |||||
5. Semester | ||||||
Elective Blocks | ||||||
Elective Block: Energy | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-1631-00L | Energy System Analysis | W | 4 credits | 3G | G. Hug, S. Hellweg, F. Noembrini, A. Schlüter | |
Abstract | The 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. | |||||
Objective | The 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. | |||||
Content | The 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 | |||||
Lecture notes | Handouts | |||||
Literature | Excerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8 | |||||
Environmental Engineering Master | ||||||
Master Studies (Programme Regulations 2016) | ||||||
Majors | ||||||
Major Urban Water Management | ||||||
Compulsory Moudules | ||||||
System Analysis in Urban Water Management | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0227-00L | Systems Analysis and Mathematical Modeling in Urban Water Management | O | 6 credits | 4G | E. Morgenroth, M. Maurer | |
Abstract | Systematic 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. | |||||
Objective | The 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. | |||||
Content | The 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) | |||||
Lecture notes | Copies of overheads will be made available. | |||||
Literature | There will be a required textbook that students need to purchase: Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg | |||||
Prerequisites / Notice | This course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously. | |||||
Major Environmental Technologies | ||||||
Compulsory Moudules | ||||||
System Analysis in Urban Water Management Note: Students taking both of the modules WASTE and SysUMW must take the course 102-0337-00 Landfilling, Contaminated Sites and Radioactive Waste Repositories in module WASTE as replacement for 102-0217-00 Process Engineering Ia being listed in both modules. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0227-00L | Systems Analysis and Mathematical Modeling in Urban Water Management | O | 6 credits | 4G | E. Morgenroth, M. Maurer | |
Abstract | Systematic 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. | |||||
Objective | The 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. | |||||
Content | The 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) | |||||
Lecture notes | Copies of overheads will be made available. | |||||
Literature | There will be a required textbook that students need to purchase: Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg | |||||
Prerequisites / Notice | This course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously. | |||||
Elective Modules For all majors. | ||||||
EM: System Analysis in Urban Water Management Elective Module for Majors "Resource Management", "River and Hydraulic Engineering" and "Water Resources Management". Note: Students taking both of the modules WASTE and SysUMW must take the course 102-0337-00 Landfilling, Contaminated Sites and Radioactive Waste Repositories in module WASTE as replacement for 102-0217-00 Process Engineering Ia being listed in both modules. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0227-00L | Systems Analysis and Mathematical Modeling in Urban Water Management | W | 6 credits | 4G | E. Morgenroth, M. Maurer | |
Abstract | Systematic 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. | |||||
Objective | The 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. | |||||
Content | The 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) | |||||
Lecture notes | Copies of overheads will be made available. | |||||
Literature | There will be a required textbook that students need to purchase: Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg | |||||
Prerequisites / Notice | This course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously. | |||||
Master Studies (Programme Regulations 2006) | ||||||
Major Courses | ||||||
Major in Urban Water Management | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0227-00L | Systems Analysis and Mathematical Modeling in Urban Water Management | O | 6 credits | 4G | E. Morgenroth, M. Maurer | |
Abstract | Systematic 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. | |||||
Objective | The 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. | |||||
Content | The 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) | |||||
Lecture notes | Copies of overheads will be made available. | |||||
Literature | There will be a required textbook that students need to purchase: Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg | |||||
Prerequisites / Notice | This course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously. | |||||
Minors | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0227-00L | Systems Analysis and Mathematical Modeling in Urban Water Management | W | 6 credits | 4G | E. Morgenroth, M. Maurer | |
Abstract | Systematic 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. | |||||
Objective | The 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. | |||||
Content | The 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) | |||||
Lecture notes | Copies of overheads will be made available. | |||||
Literature | There will be a required textbook that students need to purchase: Willi Gujer (2008): Systems Analysis for Water Technology. Springer-Verlag, Berlin Heidelberg | |||||
Prerequisites / Notice | This course will be offered together with the course Process Engineering Ia. It is advantageous to follow both courses simultaneously. | |||||
101-0187-00L | Structural Reliability and Risk Analysis | W | 3 credits | 2G | S. Marelli | |
Abstract | Structural 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. | |||||
Objective | The 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. | |||||
Content | Engineers 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. | |||||
Lecture notes | Slides 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. | |||||
Literature | Ang, 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. | |||||
Prerequisites / Notice | Basic course on probability theory and statistics | |||||
Course Units for Additional Admission Requirements The courses below are only available for MSc students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
102-0324-AAL | Ecological Systems Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 6 credits | 4R | S. Hellweg | |
Abstract | Methodological basics and application of various environmental assessment tools. | |||||
Objective | Students 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. | |||||
Content | - Methodological basics of material flow analysis, risk assessment and life cycle assessment - Application of these methods to case studies | |||||
Lecture notes | No script, but literature available on homepage. | |||||
Literature | Literature available on Link | |||||
Prerequisites / Notice | None | |||||
Environmental Sciences Bachelor | ||||||
Bachelor Studies (Programme Regulations 2016) | ||||||
Basic Courses I | ||||||
First Year Examinations | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0251-00L | Mathematics I | O | 6 credits | 4V + 2U | L. Halbeisen | |
Abstract | This course covers mathematical concepts and techniques necessary to model, solve and discuss scientific problems - notably through ordinary differential equations. | |||||
Objective | Mathematics is of ever increasing importance to the Natural Sciences and Engineering. The key is the so-called mathematical modelling cycle, i.e. the translation of problems from outside of mathematics into mathematics, the study of the mathematical problems (often with the help of high level mathematical software packages) and the interpretation of the results in the original environment. The goal of Mathematics I and II is to provide the mathematical foundations relevant for this paradigm. Differential equations are by far the most important tool for modelling and are therefore a main focus of both of these courses. | |||||
Content | 1. Single-Variable Calculus: review of differentiation, linearisation, Taylor polynomials, maxima and minima, antiderivative, fundamental theorem of calculus, integration methods, improper integrals. 2. Linear Algebra and Complex Numbers: systems of linear equations, Gauss-Jordan elimination, matrices, determinants, eigenvalues and eigenvectors, cartesian and polar forms for complex numbers, complex powers, complex roots, fundamental theorem of algebra. 3. Ordinary Differential Equations: separable ordinary differential equations (ODEs), integration by substitution, 1st and 2nd order linear ODEs, homogeneous systems of linear ODEs with constant coefficients, introduction to 2-dimensional dynamical systems. | |||||
Literature | - Thomas, G. B.: Thomas' Calculus, Part 1 (Pearson Addison-Wesley). - Bretscher, O.: Linear Algebra with Applications (Pearson Prentice Hall). | |||||
Prerequisites / Notice | Prerequisites: familiarity with the basic notions from Calculus, in particular those of function and derivative. Mathe-Lab (Assistance): Mondays 12-14, Tuesdays 17-19, Wednesdays 17-19, in Room HG E 41. | |||||
Basic Courses II | ||||||
Examination Blocks | ||||||
Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-00L | Mathematics III: Systems Analysis | O | 4 credits | 2V + 1U | N. Gruber, M. Vogt | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
Natural Science and Technical Electives | ||||||
Methodes of Statistical Data Analysis | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R efficiently for data analysis. | |||||
Content | The 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: Link | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | Basic 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 Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
Specialization in an Environmental System | ||||||
Human-Environment Systems | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0651-00L | Coevolution between Society and Environment: Analysis and Influence | W | 3 credits | 2V | J. Minsch | |
Abstract | Analysis of central mechanisms of the anthroposphere: ecological economics, theory of institutions and innovation, development economics. | |||||
Objective | Introduction to the theoretical foundations of the analysis of central mechanisms of the anthroposphere – in a sustainable development perspective. Knowledge of the different scientific and political discussions on sustainable development. Knowledge of selected analytical tools (Ecological Economics, economic analysis of institutions, innovation theory, “Ordnungstheorie”, Theory of liberal economic policy). Ability to identify central non sustainable mechanisms and policies, to formulate adequate research questions, to choose and to use adequate analytical tools, and to elaborate solutions. | |||||
Content | Sustainable development-update: origins, conceptions, state of the discussion. What's left after 25 years of discussion? Development as Freedom: Foundations of a human-rights-based Society and Economy (Amartya Sen, Daron Acemoglu / James A. Robinson, Ralf Dahrendorf, Friedrich. A. von Hayek, Karl. R. Popper. Walter Eucken). Market Economy: Its Critics, Reforms and new Developments. An Inquiry into the Nature and Causes of ...Non-Sustainability: Selected mechanisms and trends. The “neo-mercantilism-syndrom” New Trends in the Growth Debate: The Growth-spiral” (Hans Chr. Binswanger), Prosperity without growth? (T. Jackson), Intelligent Growth (R. Fücks) The Internet of Things and Collaborative Commons - on the road to "The Zero Marginal Cost Society"? Sufficiency: Perspectives of a resource-light society Corporation 2020 - Transforming Business for Tomorrow's World (Remarks on Pavan Sukhdev's bestseller) Finance Crash and Debt Crisis - new challenges for Democracy & Market Economy Resourcecurse: Resources, democracy, and economic development Globalization: Facts and elements of a fair globalization It`s the software! Institutional Innovations for Sustainable Development. Let's continue writing The Federalist Papers! On the way to the second "Great Transformation" Perspectives for further, deeper analysis | |||||
Lecture notes | skript and additional texts are distributed in the cource | |||||
Literature | A first selection: - Daron Acemoglu / James A. Robinson (2012): Why Nations Fail. The Origins of Power, Prosperity, and Poverty, New York - 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 (2005): Collapse: How Societies Choose to Fail or Succeed, New York - 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): Governing the Commons, Cambridge University Press, Cambridge / New York / Melbourne - oekom e.V., Hrsg. (2013): Baustelle Zukunft. die Grosse Trasformation von Wirtschaft und Gesellschaft, oekom Verlag, München - Karl Polanyi (1944): The Great Transformation - Karl. R. Popper (1980): Die offene Gesellschaft und ihre Feinde, Bde. I und II, 6. Auflage, Tübingen Jeremy Rifkin (2014): The Zero Mrginal Cost Society: The Internet of things, the Collaborative Commons, and the Eclipse of Capitalism, palgrave macmillan - Uwe Schneidewind / Angelika Zahrnt (2013): Damit gutes Leben einfacher wird. Perspektiven einer Suffizienzpolitik, München - Pavan Sukhdev (2012): Corporation 2020. Transforming Business for Tomorrow's World, Washington D.C. - Tomas Sedlacek (2012): Die Ökonomie von Gut und Böse, München - Amartya Sen (1999): 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): 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, Link Further reading and citations are listed in the skript and mentioned in the course. | |||||
Prerequisites / Notice | Willingness to prepare intensively the topics and to participate actively in the course | |||||
Bachelor Studies (Programme Regulations 2011) | ||||||
Basic Courses II | ||||||
Examination Blocks | ||||||
Examination Block 2 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-00L | Mathematics III: Systems Analysis | O | 4 credits | 2V + 1U | N. Gruber, M. Vogt | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
Natural Science and Technical Electives | ||||||
Natural Science Modules | ||||||
Methodes of Statistical Data Analysis | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |
Abstract | Principles 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. | |||||
Objective | Participants 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. | |||||
Content | Principles 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. | |||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||
Prerequisites / Notice | The 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. | |||||
401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R for simple data analysis. | |||||
Content | The 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: Link 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. | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W | 1.5 credits | 1G | A. Drewek, M. Mächler | |
Abstract | The 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. | |||||
Objective | The students will be able to use the software R efficiently for data analysis. | |||||
Content | The 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: Link | |||||
Lecture notes | An Introduction to R. Link | |||||
Prerequisites / Notice | Basic 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 Link Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||
Specialization in an Environmental System | ||||||
Human-Environment Systems | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0651-00L | Coevolution between Society and Environment: Analysis and Influence | W | 3 credits | 2V | J. Minsch | |
Abstract | Analysis of central mechanisms of the anthroposphere: ecological economics, theory of institutions and innovation, development economics. | |||||
Objective | Introduction to the theoretical foundations of the analysis of central mechanisms of the anthroposphere – in a sustainable development perspective. Knowledge of the different scientific and political discussions on sustainable development. Knowledge of selected analytical tools (Ecological Economics, economic analysis of institutions, innovation theory, “Ordnungstheorie”, Theory of liberal economic policy). Ability to identify central non sustainable mechanisms and policies, to formulate adequate research questions, to choose and to use adequate analytical tools, and to elaborate solutions. | |||||
Content | Sustainable development-update: origins, conceptions, state of the discussion. What's left after 25 years of discussion? Development as Freedom: Foundations of a human-rights-based Society and Economy (Amartya Sen, Daron Acemoglu / James A. Robinson, Ralf Dahrendorf, Friedrich. A. von Hayek, Karl. R. Popper. Walter Eucken). Market Economy: Its Critics, Reforms and new Developments. An Inquiry into the Nature and Causes of ...Non-Sustainability: Selected mechanisms and trends. The “neo-mercantilism-syndrom” New Trends in the Growth Debate: The Growth-spiral” (Hans Chr. Binswanger), Prosperity without growth? (T. Jackson), Intelligent Growth (R. Fücks) The Internet of Things and Collaborative Commons - on the road to "The Zero Marginal Cost Society"? Sufficiency: Perspectives of a resource-light society Corporation 2020 - Transforming Business for Tomorrow's World (Remarks on Pavan Sukhdev's bestseller) Finance Crash and Debt Crisis - new challenges for Democracy & Market Economy Resourcecurse: Resources, democracy, and economic development Globalization: Facts and elements of a fair globalization It`s the software! Institutional Innovations for Sustainable Development. Let's continue writing The Federalist Papers! On the way to the second "Great Transformation" Perspectives for further, deeper analysis | |||||
Lecture notes | skript and additional texts are distributed in the cource | |||||
Literature | A first selection: - Daron Acemoglu / James A. Robinson (2012): Why Nations Fail. The Origins of Power, Prosperity, and Poverty, New York - 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 (2005): Collapse: How Societies Choose to Fail or Succeed, New York - 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): Governing the Commons, Cambridge University Press, Cambridge / New York / Melbourne - oekom e.V., Hrsg. (2013): Baustelle Zukunft. die Grosse Trasformation von Wirtschaft und Gesellschaft, oekom Verlag, München - Karl Polanyi (1944): The Great Transformation - Karl. R. Popper (1980): Die offene Gesellschaft und ihre Feinde, Bde. I und II, 6. Auflage, Tübingen Jeremy Rifkin (2014): The Zero Mrginal Cost Society: The Internet of things, the Collaborative Commons, and the Eclipse of Capitalism, palgrave macmillan - Uwe Schneidewind / Angelika Zahrnt (2013): Damit gutes Leben einfacher wird. Perspektiven einer Suffizienzpolitik, München - Pavan Sukhdev (2012): Corporation 2020. Transforming Business for Tomorrow's World, Washington D.C. - Tomas Sedlacek (2012): Die Ökonomie von Gut und Böse, München - Amartya Sen (1999): 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): 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, Link Further reading and citations are listed in the skript and mentioned in the course. | |||||
Prerequisites / Notice | Willingness to prepare intensively the topics and to participate actively in the course | |||||
Environmental Sciences Master | ||||||
Major in Atmosphere and Climate | ||||||
Hydrology and Water Cycle | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-1253-00L | Analysis of Climate and Weather Data | W | 3 credits | 2G | C. Frei | |
Abstract | Observation 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. | |||||
Objective | Observation 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. | |||||
Content | Introduction 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. | |||||
Lecture notes | Documentation 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. | |||||
Literature | Suggested 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. | |||||
Prerequisites / Notice | Prerequisites: Atmosphäre, Mathematik IV: Statistik, Anwendungsnahes Programmieren. | |||||
Major in Ecology and Evolution | ||||||
C. Scientific Skills | ||||||
Quantitative and Computational Expertise | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-1419-00L | Analysis of Ecological Data | W | 3 credits | 2G | S. Güsewell | |
Abstract | This 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. | |||||
Objective | Students 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 | |||||
Content | - 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 | |||||
Lecture notes | Lecture notes and additional reading will be available electronically a few days before the course | |||||
Literature | Suggested 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. | |||||
Prerequisites / Notice | Time 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. | |||||
Major in Environmental Systems Policy | ||||||
Modeling and Statistical Analysis | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
860-0002-00L | Quantitative Policy Analysis and Modeling | O | 6 credits | 4G | A. Patt, T. Schmidt, E. Trutnevyte, O. van Vliet | |
Abstract | The 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 | |||||
Objective | The 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. | |||||
Minors | ||||||
Minor in Sustainable Energy Use | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-1631-00L | Energy System Analysis | W | 4 credits | 3G | G. Hug, S. Hellweg, F. Noembrini, A. Schlüter | |
Abstract | The 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. | |||||
Objective | The 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. | |||||
Content | The 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 | |||||
Lecture notes | Handouts | |||||
Literature | Excerpts from various books, e.g. K. Blok: Introduction to Energy Analysis, Techne Press, Amsterdam 2006, ISBN 90-8594-016-8 | |||||
Course Units for Additional Admission Requirements The courses below are only available for Master students with additional admission requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-0071-AAL | Mathematics III: Systems Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | N. Gruber | |
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||
Objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||
Content | Link | |||||
Lecture notes | Overhead slides will be made available through Ilias. | |||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. Link | |||||
701-1901-AAL | Systems Analysis Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement. Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit. | E- | 4 credits | 9R | N. Gruber | |
Abstract | Systems 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. | |||||
Objective | The goal of the course is to develop quantitative skills in order to understand and solve a range of typical environmental problems. | |||||
Content | The subject of the exam is the content of my undergraduate lecture series Systemanalyse I and II (see Link). This course is closely aligned with the Imboden&Koch / Imboden&Pfenniger books, except that I essentially skip chapter 7. | |||||
Lecture notes | No 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: Link German version: Link |