Suchergebnis: Katalogdaten im Herbstsemester 2018
|Data Science Master|
|401-3901-00L||Mathematical Optimization||W||11 KP||4V + 2U||R. Weismantel|
|Kurzbeschreibung||Mathematical treatment of diverse optimization techniques.|
|Lernziel||Advanced optimization theory and algorithms.|
|Inhalt||1) Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming.|
2) Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization.
3) Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory.
4) Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings, and, more generally, independence systems.
|Literatur||1) D. Bertsimas & R. Weismantel, "Optimization over Integers". Dynamic Ideas, 2005.|
2) A. Schrijver, "Theory of Linear and Integer Programming". John Wiley, 1986.
3) D. Bertsimas & J.N. Tsitsiklis, "Introduction to Linear Optimization". Athena Scientific, 1997.
4) Y. Nesterov, "Introductory Lectures on Convex Optimization: a Basic Course". Kluwer Academic Publishers, 2003.
5) C.H. Papadimitriou, "Combinatorial Optimization". Prentice-Hall Inc., 1982.
|Voraussetzungen / Besonderes||Linear algebra.|
|401-4619-67L||Advanced Topics in Computational Statistics|
Findet dieses Semester nicht statt.
|W||4 KP||2V||N. Meinshausen|
|Kurzbeschreibung||This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.|
|Lernziel||Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.|
|Inhalt||The main focus will be on graphical models in various forms: |
Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models
|Voraussetzungen / Besonderes||We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.|
|401-4623-00L||Time Series Analysis||W||6 KP||3G||N. Meinshausen|
|Kurzbeschreibung||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.
|Lernziel||Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.|
|Inhalt||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.
|Literatur||A list of references will be distributed during the course.|
|Voraussetzungen / Besonderes||Basic knowledge in probability and statistics|
|227-0101-00L||Discrete-Time and Statistical Signal Processing||W||6 KP||4G||H.‑A. Loeliger|
|Kurzbeschreibung||The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.|
|Lernziel||The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.|
|Inhalt||1. Discrete-time linear systems and filters:|
state-space realizations, z-transform and spectrum,
decimation and interpolation, digital filter design,
stable realizations and robust inversion.
2. The discrete Fourier transform and its use for digital filtering.
3. The statistical perspective:
probability, random variables, discrete-time stochastic processes;
detection and estimation: MAP, ML, Bayesian MMSE, LMMSE;
Wiener filter, LMS adaptive filter, Viterbi algorithm.
|227-0417-00L||Information Theory I||W||6 KP||4G||A. Lapidoth|
|Kurzbeschreibung||This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity.|
|Lernziel||The fundamentals of Information Theory including Shannon's source coding and channel coding theorems|
|Inhalt||The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity|
|Literatur||T.M. Cover and J. Thomas, Elements of Information Theory (second edition)|
|227-0427-00L||Signal Analysis, Models, and Machine Learning||W||6 KP||4G||H.‑A. Loeliger|
|Kurzbeschreibung||Mathematical methods in signal processing and machine learning. |
I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.
II. Learning linear and nonlinear functions and filters: neural networks, kernel methods.
III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
|Lernziel||The course is an introduction to some basic topics in signal processing and machine learning.|
|Inhalt||Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis.|
Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods.
Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
|Voraussetzungen / Besonderes||Prerequisites: |
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory
|227-0689-00L||System Identification||W||4 KP||2V + 1U||R. Smith|
|Kurzbeschreibung||Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data.|
|Lernziel||To provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity.|
|Inhalt||Introduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models.|
Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods.
Optimal experimental design, Cramer-Rao bounds, input signal design.
Parametric identification methods. On-line and batch approaches.
Closed-loop identification strategies. Trade-off between controller performance and information available for identification.
|Literatur||"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999.|
"Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977.
|Voraussetzungen / Besonderes||Control systems (227-0216-00L) or equivalent.|
|227-1033-00L||Neuromorphic Engineering I |
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.
|W||6 KP||2V + 3U||T. Delbrück, G. Indiveri, S.‑C. Liu|
|Kurzbeschreibung||This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions.|
|Lernziel||Understanding of the characteristics of neuromorphic circuit elements.|
|Inhalt||Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems.|
|Literatur||S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications.|
|Voraussetzungen / Besonderes||Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. |
Prerequisites: Background in basics of semiconductor physics helpful, but not required.
|227-0945-00L||Cell and Molecular Biology for Engineers I|
This course is part I of a two-semester course.
|W||3 KP||2G||C. Frei|
|Kurzbeschreibung||The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology.|
|Lernziel||After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested.|
|Inhalt||Lectures will include the following topics (part I and II): DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells.|
In addition, 4 journal clubs will be held, where recent publications will be discussed (2 journal clubs in part I and 2 journal clubs in part II). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 40% for the final grade.
|Skript||Scripts of all lectures will be available.|
|Literatur||"Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter.|
|261-5100-00L||Computational Biomedicine |
Number of participants limited to 60.
|W||4 KP||2V + 1U||G. Rätsch|
|Kurzbeschreibung||The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.|
|Lernziel||Over the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.|
|Inhalt||The course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine: |
1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
|Voraussetzungen / Besonderes||Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line|
|261-5112-00L||Advanced Approaches for Population Scale Compressive Genomics|
Number of participants limited to 30.
|W||3 KP||2G||A. Kahles|
|Kurzbeschreibung||Research in Biology and Medicine have been transformed into disciplines of applied data science over the past years. Not only size and inherentcomplexity of the data but also requirements on data privacy and complexity of search and access pose a wealth of new research questions.|
|Lernziel||This interactive course will explore the latest research on algorithms and data structures for population scale genomics applications and give insights into both the technical basis as well as the domain questions motivating it.|
|Inhalt||Over the duration of the semester, the course will cover three main topics. Each of the topics will consist of 70-80% lecture content and 20-30% seminar content.|
1) Algorithms and data structures for text and graph compression. Motivated through applications in compressive genomics, the course will cover succinct indexing schemes for strings, trees and general graphs, compression schemes for binary matrices as well as the efficient representation of haplotypes and genomic variants.
2) Stochastic data structures and algorithms for approximate representation of strings and graphs as well as sets in general. This includes winnowing schemes and minimizers, sketching techniques, (minimal perfect) hashing and approximate membership query data structures.
3) Data structures supporting encryption and data privacy. As an extension to data structures discussed in the earlier topics, this will include secure indexing using homomorphic encryption as well as design for secure storage and distribution of data.
|636-0017-00L||Computational Biology||W||6 KP||3G + 2A||T. Stadler, C. Magnus, T. Vaughan|
|Kurzbeschreibung||The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.|
|Lernziel||Attendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are:|
* stochastic models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
Attendees will apply these concepts to a number of applications yielding biological insight into:
* pathogen evolution
* macroevolution of species
|Inhalt||The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.|
|Skript||Lecture slides will be available on moodle.|
|Literatur||The course is not based on any of the textbooks below, but they are excellent choices as accompanying material:|
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
|Voraussetzungen / Besonderes||Basic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date http://www.cbb.ethz.ch/news-events.html |
For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course.
|701-0023-00L||Atmosphäre||W||3 KP||2V||E. M. Fischer, T. Peter|
|Kurzbeschreibung||Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht.|
|Lernziel||Verständnis grundlegender physikalischer und chemischer Prozesse in der Atmosphäre. Kenntnis über die Mechanismen und Zusammenhänge von: Wetter - Klima, Atmosphäre - Ozeane - Kontinente, Troposphäre - Stratosphäre. Verständnis von umweltrelevanten Strukturen und Vorgängen in sehr unterschiedlichem Massstab. Grundlagen für eine modellmässige Darstellung komplexer Zusammenhänge in der Atmosphäre.|
|Inhalt||Grundlagen der Atmosphäre, physikalischer Aufbau und chemische Zusammensetzung, Spurengase, Kreisläufe in der Atmosphäre, Zirkulation, Stabilität, Strahlung, Kondensation, Wolken, Oxidationspotential und Ozonschicht.|
|Skript||Schriftliche Unterlagen werden abgegeben.|
|Literatur||- John H. Seinfeld and Spyros N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, New York, 1998.|
- Gösta H. Liljequist, Allgemeine Meteorologie, Vieweg, Braunschweig, 1974.
|701-0473-00L||Wettersysteme||W||3 KP||2G||M. A. Sprenger, F. Scholder-Aemisegger|
|Kurzbeschreibung||Satellitenbeobachtungen; Analyse vertikaler Sondierungen; Geostrophischer und thermischer Wind; Tiefdruckwirbel in den mittleren Breiten; globalen Zirkulation; Nordatlantische Oszillation; Atmosphärische Blockierungswetterlagen; Eulersche und Lagrange Perspektive der Dynamik;|
Potentielle Vortizität; Alpine Dynamik (Windstürme, Um- und Überströmung von Gebirgen); Planetare Grenzschicht
|Lernziel||Die Studierenden können:|
- die gängigen Mess- und Analysemethoden der Atmosphärendynamik erklären
- mathematische Grundlagen der Atmosphärendynamik beispielhaft erklären
- die Dynamik von globalen und synoptisch-skaligen Prozessen erklären
- den Einfluss von Gebirgen auf die Atmosphärendynamik erklären
|Inhalt||Satellitenbeobachtungen; Analyse vertikaler Sondierungen; Geostrophischer und thermischer Wind; Tiefdruckwirbel in den mittleren Breiten; Überblick und Energetik der globalen Zirkulation; Nordatlantische Oszillation; Atmosphärische Blockierungswetterlagen; Eulersche und Lagrange Perspektive der Dynamik;|
Potentielle Vortizität; Alpine Dynamik (Windstürme, Um- und Überströmung von Gebirgen); Planetare Grenzschicht
|Skript||Vorlesungsskript + Folien|
|Literatur||Atmospheric Science, An Introductory Survey|
John M. Wallace and Peter V. Hobbs, Academic Press
|701-1251-00L||Land-Climate Dynamics |
Number of participants limited to 36.
|W||3 KP||2G||S. I. Seneviratne, E. L. Davin|
|Kurzbeschreibung||The purpose of this course is to provide fundamental background on the role of land surface processes (vegetation, soil moisture dynamics, land energy and water balances) in the climate system. The course consists of 2 contact hours per week, including lectures, group projects and computer exercises.|
|Lernziel||The students can understand the role of land processes and associated feedbacks in the climate system.|
|Skript||Powerpoint slides will be made available|
|Voraussetzungen / Besonderes||Prerequisites: Introductory lectures in atmospheric and climate science |
Atmospheric physics -> Link
Climate systems -> Link
|101-0417-00L||Transport Planning Methods||W||6 KP||4G||K. W. Axhausen|
|Kurzbeschreibung||The course provides the necessary knowledge to develop models supporting and also evaluating the solution of given planning problems. |
The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis.
|Lernziel||- Knowledge and understanding of statistical methods and algorithms commonly used in transport planning|
- Comprehend the reasoning and capabilities of transport models
- Ability to independently develop a transport model able to solve / answer planning problem
- Getting familiar with cost-benefit analysis as a decision-making supporting tool
|Inhalt||The course provides the necessary knowledge to develop models supporting the solution of given planning problems and also introduces cost-benefit analysis as a decision-making tool. Examples of such planning problems are the estimation of traffic volumes, prediction of estimated utilization of new public transport lines, and evaluation of effects (e.g. change in emissions of a city) triggered by building new infrastructure and changes to operational regulations.|
To cope with that, the problem is divided into sub-problems, which are solved using various statistical models (e.g. regression, discrete choice analysis) and algorithms (e.g. iterative proportional fitting, shortest path algorithms, method of successive averages).
The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. Interim lab session take place regularly to guide and support students with the applied part of the course.
|Skript||Moodle platform (enrollment needed)|
|Literatur||Willumsen, P. and J. de D. Ortuzar (2003) Modelling Transport, Wiley, Chichester.|
Cascetta, E. (2001) Transportation Systems Engineering: Theory and Methods, Kluwer Academic Publishers, Dordrecht.
Sheffi, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice Hall, Englewood Cliffs.
Schnabel, W. and D. Lohse (1997) Verkehrsplanung, 2. edn., vol. 2 of Grundlagen der Strassenverkehrstechnik und der Verkehrsplanung, Verlag für Bauwesen, Berlin.
McCarthy, P.S. (2001) Transportation Economics: A case study approach, Blackwell, Oxford.
|101-0491-00L||Agent Based Modeling in Transportation||W||6 KP||4G||T. J. P. Dubernet, M. Balac|
|Kurzbeschreibung||This lectures provides a round tour of agent based models for transportation policy analysis. First, it introduces statistical methods to combine heterogeneous data sources in a usable representation of the population. Then, agent based models are described in details, and applied in a case study.|
|Lernziel||At the end of the course, the students should:|
- be aware of the various data sources available for mobility behavior analysis
- be able to combine those data sources in a coherent representation of the transportation demand
- understand what agent based models are, when they are useful, and when they are not
- have working knowledge of the MATSim software, and be able to independently evaluate a transportation problem using it
|Inhalt||This lecture provides a complete introduction to agent based models for transportation policy analysis. Two important topics are covered:|
1) Combination of heterogeneous data sources to produce a representation of the transport system
At the center of agent based models and other transport analyses is the synthetic population, a statistically realistic representation of the population and their transport needs.
This part will present the most common types of data sources and statistical methods to generate such a population.
2) Use of Agent-Based methods to evaluate transport policies
The second part will introduce the agent based paradigm in details, including tradeoffs compared to state-of-practice methods.
An important part of the grade will come from a policy analysis to carry with the MATSim open-source software, which is developed at ETH Zurich and TU Berlin and gets used more and more by practitioners, notably the Swiss rail operator SBB.
|Literatur||Agent-based modeling in general|
Helbing, D (2012) Social Self-Organization, Understanding Complex Systems, Springer, Berlin.
Heppenstall, A., A. T. Crooks, L. M. See and M. Batty (2012) Agent-Based Models of Geographical Systems, Springer, Dordrecht.
Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London
Additional relevant readings, mostly scientific articles, will be recommended throughout the course.
|Voraussetzungen / Besonderes||There are no strict preconditions in terms of which lectures the students should have previously attended. However, knowledge of basic statistical theory is expected, and experience with high-level programming languages (Java, R, Python...) is useful.|
|103-0227-00L||Cartography III||W||5 KP||4G||L. Hurni|
|Kurzbeschreibung||Grundlegende Methoden, Technologien, Systeme und Programmierung in der interaktiven Internet-Kartografie.|
|Lernziel||Kenntnisse über die grundlegenden Methoden, Technologien, Programmierung und Systeme in der interaktiven Internet-Kartografie erwerben. Bestehende Produkte bezüglich der angewendeten Produktionsmethoden beurteilen können und sinnvolle Methoden für konkrete Web-basierte Kartenprojekte bestimmen können.|
- Web Map Services (WMS)
- Symbolisierung von Internet-Karten
- Kartenerstellung mit GIS-Daten
- 3D-Anwendungen in der Kartografie
|Skript||Ein eigenes Skript zur Vorlesung und Übungsanleitungen werden abgegeben.|
|Literatur||- Grünreich, Dietmar, Hake, Günter and Liqiu Meng (2002): Kartographie, 8. Auflage, Verlag W. de Gruyter, Berlin|
- Robinson, Arthur et al. (1995): Elements of Cartography, 6th edition, John Wiley & Sons, New York, ISBN 0-471-55579-7
- Jones, Christopher (1997): Geographical Information Systems (GIS) and Computer Cartography, Longman, Harlow, ISBN 0-582-04439-1
- Stoll, Heinz (2001): Computergestützte Kartografie, SGK-Publikation Nr. 15 (siehe www.kartographie.ch)
|Voraussetzungen / Besonderes||Voraussetzungen: Kartografie I; Kartografie II; Thematische Kartografie|
Weitere Informationen unter http://www.karto.ethz.ch/studium/lehrangebot.html
|103-0237-00L||GIS III||W||5 KP||3G||M. Raubal|
|Kurzbeschreibung||The course deals with advanced topics in GIS: GIS project lifecycle, Managing GIS, Legal issues, GIS assets & constraints; Geospatial Web Services; Geostatistics; Geosimulation; Human-Computer Interaction; Cognitive Issues in GIS.|
|Lernziel||Students will get a detailed overview of advanced GIS topics. They will go through all steps of setting up a Web-GIS application in the labs and perform other practical tasks relating to Geosimulation, Human-Computer Interaction, Geostatistics, and Web Processing Services.|
|Skript||Lecture slides will be made available in digital form.|
|Literatur||Fu, P. and Sun, J., Web GIS - Principles and Applications (2011), ESRI Press, Redlands, California.|
O'Sullivan, D., & Unwin, D. (2010). Geographic Information Analysis (second ed.). Hoboken, New Jersey: Wiley.
|103-0778-00L||GIS and Geoinformatics Lab||W||4 KP||4P||M. Raubal|
|Kurzbeschreibung||Independent study project with (mobile) geoinformation technologies.|
|Lernziel||Learn how to work with (mobile) geoinformation technologies (including application design and programming).|
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