Suchergebnis: Katalogdaten im Herbstsemester 2014
|Vertiefung in Computational Science|
|Kernfächer der Vertiefung Computational Science|
|252-0523-00L||Computational Biology||W||6 KP||3V + 2U||G. H. Gonnet|
|Kurzbeschreibung||Study of computational techniques, algorithms and data structures used to solve problems in computational biology. Topics: basic biology, string alignment, phylogeny (distance, character, parsimony), molecular evolution, multiple sequence alignment, probabilistic and statistical models, Markov models, microarrays, dynamic programming, maximum likelihood and specialized DNA and protein analysis.|
|Lernziel||Familiarize the students with the basic concepts of molecular biology and the models and algorithms used to understand, classify and predict behaviour of living organism. This course is at the most basic level, where the main issues, mostly of molecular sequences, are studied.|
|Inhalt||This course lies in the intersection between Computer Science and Molecular Biology. The main purpose is to study computational techniques, algorithms and data structures which are usually applied to solve problems in Molecular Biology and Biochemistry.|
The following topics are likely to be covered: Introduction, mathematical models of evolution, protein and DNA sequence alignment and its meaning, phylogenetic tree construction, multiple sequence alignments, secondary structure prediction, molecular dynamics, threading, role of bioinformatics in drug design, etc. From the computer science point of view we concentrate our attention in practical solutions for the above problems. Biological knowledge is an asset but not a prerequisite.
|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 Computational Science|
|252-0535-00L||Machine Learning||W||6 KP||3V + 2U||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 a 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||Solid basic knowledge in analysis, statistics and numerical methods for|
CSE. Experience in programming for solving the project tasks.
|252-0543-01L||Computer Graphics||W||6 KP||3V + 2U||M. Gross, O. Sorkine Hornung|
|Kurzbeschreibung||This course covers some of the fundamental concepts of computer graphics. The two main parts of the class are image synthesis and geometric modeling.|
|Lernziel||At the end of the course students will be able to design and implement a rendering system based on raytracing. You will study the basic principles of modeling with splines and integrate spline-based representations into a rendering system. In addition we want to stimulate your curiosity to explore the field of computer graphics on your own or in future courses.|
|Inhalt||This course covers some of the fundamental concepts of computer graphics. The two main parts of the class are rendering and modeling. In the first part, we will discuss the basics of photorealistic image synthesis, i.e. how to generate a realistic image from a digital representation of a 3D scene. After introducing raytracing, we will briefly look at the physics of light transport, discuss the rendering equation, and investigate some advanced techniques to enhance the realism of rendered images. The second part will introduce the basics of modeling with curves and surfaces. We will discuss Bezier curves and surfaces, B-Splines and NURBS, and show how they can be used to design complex 3D geometry.|
|Voraussetzungen / Besonderes||Prerequisites:|
|263-5001-00L||Introduction to Finite Elements and Sparse Linear System Solving||W||4 KP||2V + 1U||P. Arbenz, T. Kaman|
|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.
|263-5150-00L||Scientific Databases||W||4 KP||2V + 1U||G. H. Gonnet|
|Kurzbeschreibung||Scientific databases share many aspects with classical DBs, but have additional specific aspects. We will review Relational DBs, Object Oriented DBs, Knowledge DBs, textual DBs and the Semantic Web. All these topics will be studied from the point of view of the scientific applications (Bioinformatics, Physics, Chemistry, Health, Engineering) A toy SDB will be used for exercises.|
|Lernziel||The goals of this course are to:|
(a) Familiarize the students with how existing DBs can be used for
(b) Recognize the areas where SciDBs differ and require additional
features compared to classical DBs.
(c) Be able to understand more easily SciDBs, improve existing ones
or design/create new ones.
(d) Familiarize the students with at least two examples of SciDBs.
|Inhalt||1) - Introduction, Statement of the problem, course structure, exercises,|
why Scientific DBs (SDBs) do not fit exactly the classical DB area.
Hierarchy: File systems, data bases, knowledge bases and variations.
Efficiency issues and how they differ from classical DB.
2) - Relational DB used for scientific data, pros/cons
Introduction to RDB, limitations of the model, basics of SQL,
handling of metadata, examples of scientific use of RDBs.
3) - Object Oriented DB. Rich/structured objects are very appealing
in SDB. OODB primitives and environments. OODB searching.
Space and access time efficiency of OODBs.
4) - Knowledge bases, key-value stores, ontologies, workflow-based
5) - MapReduce / Hadoop
6) - Storing and sharing mathematical objects, Open Math, its relation
with OODB and Knowledge bases. Also the problem of chemical
7) - SGML and XML, human-readable databases, genomic databases.
Advantages of human-readable databases (the huge initial success
of genomic databases).
8) - Semantic web, Resource Description Framework (RDF) triples, SparQL.
An example of very flexible database for knowlege storage. Goals of
the Semantic Web, discussion about its future.
9) - An ideal scenario (and the design of a toy system with most of the
desired features for exploration and exercises).
10) - Automatic dependency management, (make and similar). The graph
theory problem. Critical paths.
11) - Functional testing, Verifiers, Consistency, Short-circuit testing,
Recovery and Automatic recovery, Backup (incremental) methods.
12) - Performance and space issues, various uses of compression,
concurrency control. Hardware issues, clusters, Cloud computing,
13) - Guest speaker: Ioannis Xenarios (UniProtKB/Swiss-Prot).
|Literatur||Several papers and online articles will be made available.|
There is no single textbook for this course.
A significant amount of material will be delivered in the lectures making lecture attendance highly recommended.
|Seminar Computational Science|
|252-5701-00L||Advanced Topics in Computer Graphics and Vision||W||2 KP||2S||M. Gross, M. Pollefeys, 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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