Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Search result: Catalogue data in Spring Semester 2015

Computer Science Master Information
Focus Courses
Focus Courses in Visual Computing
Seminar in Visual Computing
252-5704-00LAdvanced Methods in Computer Graphics Information Restricted registration - show details
Number of participants limited to 24.
W2 credits2SM. Gross, O. Sorkine Hornung
AbstractThis seminar covers advanced topics in computer graphics with a focus on the latest research results. Topics include modeling, rendering,
animation, physical simulation, computational photography, and others.
ObjectiveThe goal is to obtain an in-depth understanding of actual problems and
research topics in the field of computer graphics as well as improve
presentation and critical analysis skills.
Computer Science Elective Courses
The Elective Computer Science Courses can be selected from all Master level courses offered by D-INFK.
252-0820-00LCase Studies from Practice Information W4 credits2V + 1UM. Brandis
AbstractThe course is designed to provide students with an understanding of "real-life" challenges from business settings and teach them how to address these.
ObjectiveBy using case studies that are based on actual IT projects, students will learn how to deal with complex, not straightforward problems. It will help them to apply their theoretical Computer Science background in practice and will teach them fundamental principles of IT management and challenges with IT in practice.
ContentThe course consists of multiple lectures about general IT management topics held by Marc Brandis and case studies provided by guest lecturers from either IT companies or IT departments of a diverse range of companies.
Presenting companies so far include Deloitte (how to develop innovative technology solutions for a luxury retailer), Selfnation (lessons learned from a startup company), Credit Suisse (investment banking case), HP (business continuity management), 28msec (product pricing in a software startup company), Open Web Technology (strategic choices in software development), and Marc Brandis Strategic Consulting (various).
263-0600-00LResearch in Computer Science Restricted registration - show details
Only for Computer Science MSc.
W5 credits11AProfessors
AbstractIndependent project work under the supervision of a Computer Science Professor.
Objectivesee above
Prerequisites / NoticeOnly students who fulfill one of the following requirements are allowed to begin a research project:
a) 1 lab (interfocus course) and 1 core focus course
b) 2 core focus courses
c) 2 labs (interfocus courses)

A task description must be submitted to the Student Administration Office at the beginning of the work.
272-0300-00LAlgorithmics for Hard Problems Information
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A.
W4 credits2V + 1UJ. Hromkovic, H.‑J. Böckenhauer, D. Komm
AbstractThis course unit looks into algorithmic approaches to the solving of hard problems. The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools.
ObjectiveTo systematically acquire an overview of the methods for solving hard problems.
ContentFirst, the concept of hardness of computation is introduced (repeated for the computer science students). Then some methods for solving hard problems are treated in a systematic way. For each algorithm design method, it is discussed what guarantees it can give and how we pay for the improved efficiency.
Lecture notesUnterlagen und Folien werden zur Verfügung gestellt.
LiteratureJ. Hromkovic: Algorithmics for Hard Problems, Springer 2004.

R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006.

F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010.
272-0302-00LApproximation and Online Algorithms Information W4 credits2V + 1UH.‑J. Böckenhauer, D. Komm
AbstractThis lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches.
ObjectiveGet a systematic overview of different methods for designing approximative algorithms for hard optimization problems and online problems. Get to know methods for showing the limitations of these approaches.
ContentApproximation algorithms are one of the most succesful techniques to attack hard optimization problems. Here, we study the so-called approximation ratio, i.e., the ratio of the cost of the computed approximating solution and an optimal one (which is not computable efficiently).
For an online problem, the whole instance is not known in advance, but it arrives pieceweise and for every such piece a corresponding part of the definite output must be given. The quality of an algorithm for such an online problem is measured by the competitive ratio, i.e., the ratio of the cost of the computed solution and the cost of an optimal solution that could be given if the whole input was known in advance.

The contents of this lecture are
- the classification of optimization problems by the reachable approximation ratio,
- systematic methods to design approximation algorithms (e.g., greedy strategies, dynamic programming, linear programming relaxation),
- methods to show non-approximability,
- classic online problem like paging or scheduling problems and corresponding algorithms,
- randomized online algorithms,
- the design and analysis principles for online algorithms, and
- limits of the competitive ratio and the advice complexity as a way to do a deeper analysis of the complexity of online problems.
LiteratureDie Vorlesung orientiert sich teilweise an folgenden Büchern:

J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004

A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998

D. Komm: Advice and Randomization in Online Computation, 2012
401-3632-00LComputational Statistics Information W10 credits3V + 2UM. Mächler, P. L. Bühlmann
Abstract"Computational Statistics" deals with modern methods of data analysis (aka "data science") for prediction and inference. An overview of existing methodology is provided and also by the exercises, the student is taught to choose among possible models and about their algorithms and to validate them using graphical methods and simulation based approaches.
ObjectiveGetting to know modern methods of data analysis for prediction and inference.
Learn to choose among possible models and about their algorithms.
Validate them using graphical methods and simulation based approaches.
ContentCourse Synopsis:
multiple regression, nonparametric methods for regression and classification (kernel estimates, smoothing splines, regression and classification trees, additive models, projection pursuit, neural nets, ridging and the lasso, boosting). Problems of interpretation, reliable prediction and the curse of dimensionality are dealt with using resampling, bootstrap and cross validation.
Details are available via .

Exercises will be based on the open-source statistics software R ( Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended.
More details are available via the webpage (-> "Computational Statistics").
Lecture noteslecture notes are available online; see (-> "Computational Statistics").
Literature(see the link above, and the lecture notes)
Prerequisites / NoticeBasic "applied" mathematical calculus and linear algebra.
At least one semester of (basic) probability and statistics.
272-0301-00LMethods for Design of Random Systems Information
Does not take place this semester.
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science B.
W4 credits2V + 1UJ. Hromkovic
AbstractThe students should get a deep understanding of the notion of randomness and its usefulness. Using basic elements probability theory and number theory the students will discover randomness as a source of efficiency in algorithmic. The goal is to teach the paradigms of design of randomized algorithms.
ObjectiveTo understand the computational power of randomness and to learn the basic
methods for designing randomized algorithms
Lecture notesJ. Hromkovic: Randomisierte Algorithmen, Teubner 2004.

J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006.

J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
LiteratureJ. Hromkovic: Randomisierte Algorithmen, Teubner 2004.

J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006.

J.Hromkovic: Algorithmics for Hard Problems, Springer 2004.
Elective Courses
Students can individually chose from the entire Master course offerings from ETH Zurich, EPF Lausanne, the University of Zurich and - but only with the consent of the Director of Studies - from all other Swiss universities.

For further details please see Art. 31 of the Regulations 2009 for the Master Program in Computer Science.
252-0700-00LInternship Information Restricted registration - show details
Only for Computer Science MSc.
W0 creditsexternal organisers
AbstractInternship in a computer science company, which is admitted by the CS Department at ETH. Minimum 10 weeks fulltime employment.
ObjectiveThe main objective of the 10-week internship is to expose students to the industrial work environment. During this period, students have the opportunity to be involved in on-going projects at the host institution.
Prerequisites / NoticeA task description must be presented for approval, before the start of the internship. After completion of the internship, a work certificate must be presented.
Compulsory Electives in Humanities, Social and Political Sciences
» see GESS Compulsory Electives
Master Thesis
263-0800-00LMaster's Thesis Information Restricted registration - show details
Only students who fulfill the following criteria are allowed to begin with their master thesis:
a. successful completion of the bachelor programme;
b. fulfilling any additional requirements necessary to gain admission to the master programme;
c. "Inter focus courses" (12 credits) completed;
d. "Focus courses" (26 credits) completed.
O30 credits64DProfessors
AbstractIndependent project work supervised by a Computer Science professor. Duration 6 months.
ObjectiveTo work independently and to produce a scientifically structured work under the supervision of a Computer Science Professor.
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