Suchergebnis: Katalogdaten im Herbstsemester 2017
|Vertiefung in Computational Science|
|Kernfächer der Vertiefung in Computational Science|
|252-0535-00L||Machine Learning||W||8 KP||3V + 2U + 2A||J. M. Buhmann|
|Kurzbeschreibung||Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.|
|Lernziel||Students will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. A machine learning project will provide an opportunity to test the machine learning algorithms on real world data.|
|Inhalt||The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.|
Topics covered in the lecture include:
- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
|Skript||No lecture notes, but slides will be made available on the course webpage.|
|Literatur||C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.|
R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.
L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
|Voraussetzungen / Besonderes||The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.|
Students should at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution.
|636-0007-00L||Computational Systems Biology||W||6 KP||3V + 2U||J. Stelling|
|Kurzbeschreibung||Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).|
|Lernziel||The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.|
|Inhalt||Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.|
|Literatur||U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.|
Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006.
|Wahlfächer der Vertiefung in Computational Science|
|252-0543-01L||Computer Graphics||W||6 KP||3V + 2U||M. Gross, J. Novak|
|Kurzbeschreibung||This course covers some of the fundamental concepts of computer graphics, namely 3D object representations and generation of photorealistic images from digital representations of 3D scenes.|
|Lernziel||At the end of the course the students will be able to build a rendering system. The students will study the basic principles of rendering and image synthesis. In addition, the course is intended to stimulate the students' curiosity to explore the field of computer graphics in subsequent courses or on their own.|
|Inhalt||This course covers fundamental concepts of modern computer graphics. Students will learn about 3D object representations and the details of how to generate photorealistic images from digital representations of 3D scenes. Starting with an introduction to 3D shape modeling and representation, texture mapping and ray-tracing, we will move on to acceleration structures, the physics of light transport, appearance modeling and global illumination principles and algorithms. We will end with an overview of modern image-based image synthesis techniques, covering topics such as lightfields and depth-image based rendering.|
|Voraussetzungen / Besonderes||Prerequisites:|
Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Visual Computing course recommended.
The programming assignments will be in C++. This will not be taught in the class.
|263-5001-00L||Introduction to Finite Elements and Sparse Linear System Solving||W||4 KP||2V + 1U||P. Arbenz|
|Kurzbeschreibung||The finite element (FE) method is the method of choice for (approximately) solving partial differential equations on complicated domains. In the first third of the lecture, we give an introduction to the method. The rest of the lecture will be devoted to methods for solving the large sparse linear systems of equation that a typical for the FE method. We will consider direct and iterative methods.|
|Lernziel||Students will know the most important direct and iterative solvers for sparse linear systems. They will be able to determine which solver to choose in particular situations.|
|Inhalt||I. THE FINITE ELEMENT METHOD|
(1) Introduction, model problems.
(2) 1D problems. Piecewise polynomials in 1D.
(3) 2D problems. Triangulations. Piecewise polynomials in 2D.
(4) Variational formulations. Galerkin finite element method.
(5) Implementation aspects.
II. DIRECT SOLUTION METHODS
(6) LU and Cholesky decomposition.
(7) Sparse matrices.
(8) Fill-reducing orderings.
III. ITERATIVE SOLUTION METHODS
(9) Stationary iterative methods, preconditioning.
(10) Preconditioned conjugate gradient method (PCG).
(11) Incomplete factorization preconditioning.
(12) Multigrid preconditioning.
(13) Nonsymmetric problems (GMRES, BiCGstab).
(14) Indefinite problems (SYMMLQ, MINRES).
|Literatur|| M. G. Larson, F. Bengzon: The Finite Element Method: Theory, Implementation, and Applications. Springer, Heidelberg, 2013.|
 H. Elman, D. Sylvester, A. Wathen: Finite elements and fast iterative solvers. OUP, Oxford, 2005.
 Y. Saad: Iterative methods for sparse linear systems (2nd ed.). SIAM, Philadelphia, 2003.
 T. Davis: Direct Methods for Sparse Linear Systems. SIAM, Philadelphia, 2006.
 H.R. Schwarz: Die Methode der finiten Elemente (3rd ed.). Teubner, Stuttgart, 1991.
|Voraussetzungen / Besonderes||Prerequisites: Linear Algebra, Analysis, Computational Science.|
The exercises are made with Matlab.
|636-0017-00L||Computational Biology||W||6 KP||3G + 2A||C. Magnus, T. Stadler, 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 phylodynamics is to understand and quantify the 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 "Central Element". We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills.|
|Seminar in Computational Science|
|252-5701-00L||Advanced Topics in Computer Graphics and Vision |
Maximale Teilnehmerzahl: 24
|W||2 KP||2S||M. Gross, O. Sorkine Hornung|
|Kurzbeschreibung||This seminar covers advanced topics in computer graphics, such as modeling, rendering, animation, real-time graphics, physical simulation, and computational photography. Each time the course is offered, a collection of research papers is selected and each student presents one paper to the class and leads a discussion about the paper and related topics.|
|Lernziel||The goal is to get an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentations and critical analysis skills.|
|Inhalt||This seminar covers advanced topics in computer graphics,|
including both seminal research papers as well as the latest
research results. Each time the course is offered, a collection of
research papers are selected covering topics such as modeling,
rendering, animation, real-time graphics, physical simulation, and
computational photography. Each student presents one paper to the
class and leads a discussion about the paper and related topics.
All students read the papers and participate in the discussion.
|Literatur||Individual research papers are selected each term. See http://graphics.ethz.ch/ for the current list.|
|Voraussetzungen / Besonderes||Prerequisites: |
The courses "Computer Graphics I and II" (GDV I & II) are recommended, but not mandatory.
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