Suchergebnis: Katalogdaten im Herbstsemester 2014

CAS in Informatik Information
Obligatorische Fächer der Vertiefung
Lerneinheiten für die Zulassungsprüfung ergeben keine ECTS Punkte und werden nicht zum Zertifikatslehrgang Informatik angerechnet.

Die Lehrveranstaltung 151-0107-20L High Performance Computing for Science and Engineering (HPCSE) for Engineers I (HS) kann nur mit der Lehrveranstaltung 401-0686-10L High Performance Computing for Science and Engineering (HPCSE) for Engineers II (FS) zusammen (8 KP) als obligatorisches Fach der Vertiefung angerechnet werden.
NummerTitelTypECTSUmfangDozierende
252-0206-00LVisual Computing Information W8 KP4V + 3UM. Gross, M. Pollefeys
KurzbeschreibungThis course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow.
LernzielThis course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia and computer vision. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH.
InhaltCourse topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression.

In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms.
SkriptA scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks.
LiteraturMarkus Gross: Computer Graphics, scriptum, 1994-2005
252-0209-00LAlgorithms, Probability, and Computing Information W8 KP4V + 2U + 1AE. Welzl, T.  Holenstein, P. Widmayer
KurzbeschreibungAdvanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
LernzielStudying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.
SkriptWill be handed out.
LiteraturIntroduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest;
Randomized Algorithms by R. Motwani und P. Raghavan;
Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
252-0210-00LCompiler DesignW8 KP4V + 3UT. Gross
KurzbeschreibungDiese Vorlesung benutzt Compiler als Beispiel für moderne Software Entwicklung. Dazu werden die Kernthemen des Compilerbaus behandelt: Syntax Analyse, Symboltabellen, Code Erzeugung. Die Vorlesung und Uebungen geben den Studierenden eine gute Gelegenheit, Muster in diversen Kontexten anzuwenden.
LernzielLearn principles of compiler design, gain practical experience designing and implementing a medium-scale software system.
InhaltThis course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools (parser generators); the implementation language is Java. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project.
Specific topics: Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: expression evaluation, straight line code, conditionals, loops, procedure calls, simple register allocation techniques. Storage allocation on the stack, parameter passing, runtime storage management, heaps. Special topics as time permits: introduction to global dataflow and its application to register allocation, instruction scheduling.
LiteraturAho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition)

Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997
Voraussetzungen / BesonderesPrerequisites:
Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level.
252-0213-00LVerteilte Systeme Information W8 KP6G + 1AF. Mattern, R. Wattenhofer
KurzbeschreibungVerteilte Kontrollalgorithmen (wechselseitiger Ausschluss, logische Uhren), Kommunikationsmodelle (RPC, synchrone/asynchrone Kommunikation, Broadcast, Ereignisse, Tupelräume), Middleware, Service- und Ressourcen-orientierte Architekturen (SOAP, REST), Sicherheit, Fehlertoleranz (Modelle, Consensus), Replikation (Primary Copy, 2PC, 3PC, Quorum-Systeme), Shared Memory (Spin Locks, Concurrency).
LernzielKennenlernen von wesentlichen Technologien und Architekturen verteilter Systeme.
InhaltWir geben eine Einführung in verteilte Systeme (Charakteristika und Konzepte) und diskutieren sodann verteilte Kontrollalgorithmen (Flooding-Verfahren, wechselseitiger Ausschluss, logische Uhren), Basis-Kommunikationsmodelle (Remote-Procedure-Call, Client-Server-Strukturen, synchrone und asynchrone Kommunikation), abstraktere Kommunikationsprinzipien (Broadcast, Ereignisse, Tupelräume), Namensverwaltung, Middleware und Techniken offener Systeme (z.B. REST, SOAP), Infrastruktur für spontan vernetzte Systeme (JINI), Cloud-Computing sowie Sicherheits- und Schutzmechanismen. Da partielle Systemausfälle charakteristisch für verteilte Systeme sind, werden auch Fehlermodelle und Fehlertoleranz-Algorithmen zum systematischen Umgang mit Fehlersituationen besprochen. Wir diskutieren dazu Fehlertoleranzaspekte (Modelle, Consensus, Agreement) sowie Replikationsaspekte (Primary Copy, 2PC, 3PC, Paxos, Quorum-Systeme, verteilter Speicher) und Probleme bei asynchronen Multiprozesssystemen (Shared Memory, Spin Locks, Concurrency). Parallel zur Vorlesung werden einige der Übungen in Form praktischer mehrwöchiger Aufgaben durchgeführt, wobei die Teilnehmer mit der Programmierung von mobilen Plattformen (smartphones) und nachrichtenbasierten Kommunikationsprinzipien vertraut werden.
151-0107-20LHigh Performance Computing for Science and Engineering (HPCSE) IW4 KP4GP. Koumoutsakos, M. Troyer
KurzbeschreibungThis course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering.
LernzielIntroduction to HPC for scientists and engineers
Fundamental of:
1. Parallel Computing Architectures
2. MultiCores
3. ManyCores
InhaltProgramming models and languages:
1. C++ threading (2 weeks)
2. OpenMP (4 weeks)
3. MPI (5 weeks)

Computers and methods:
1. Hardware and architectures
2. Libraries
3. Particles: N-body solvers
4. Fields: PDEs
5. Stochastics: Monte Carlo
Skripthttp://www.cse-lab.ethz.ch/index.php/teaching/42-teaching/classes/615-hpcse1
Class notes, handouts
Fokusfächer und Wahlfächer
NummerTitelTypECTSUmfangDozierende
252-0237-00LConcepts of Object-Oriented ProgrammingW6 KP3V + 2UP. Müller
KurzbeschreibungCourse that focuses on an in-depth understanding of object-oriented programming and compares designs of object-oriented programming languages. Topics include different flavors of type systems, inheritance models, encapsulation in the presence of aliasing, object and class initialization, program correctness, reflection
LernzielAfter this course, students will:
Have a deep understanding of advanced concepts of object-oriented programming and their support through various language features. Be able to understand language concepts on a semantic level and be able to compare and evaluate language designs.
Be able to learn new languages more rapidly.
Be aware of many subtle problems of object-oriented programming and know how to avoid them.
InhaltThe main goal of this course is to convey a deep understanding of the key concepts of sequential object-oriented programming and their support in different programming languages. This is achieved by studying how important challenges are addressed through language features and programming idioms. In particular, the course discusses alternative language designs by contrasting solutions in languages such as C++, C#, Eiffel, Java, Python, and Scala. The course also introduces novel ideas from research languages that may influence the design of future mainstream languages.

The topics discussed in the course include among others:
The pros and cons of different flavors of type systems (for instance, static vs. dynamic typing, nominal vs. structural, syntactic vs. behavioral typing)
The key problems of single and multiple inheritance and how different languages address them
Generic type systems, in particular, Java generics, C# generics, and C++ templates
The situations in which object-oriented programming does not provide encapsulation, and how to avoid them
The pitfalls of object initialization, exemplified by a research type system that prevents null pointer dereferencing
How to maintain the consistency of data structures
LiteraturWill be announced in the lecture.
Voraussetzungen / BesonderesPrerequisites:
Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience
252-0239-00LSoftware VerificationW6 KP3V + 2UB. Meyer, C. A. Furia, S. Nanz
KurzbeschreibungThis course surveys some of the main approaches to software verification, including axiomatic semantics, abstract interpretation, model checking, and testing.
LernzielAfter successfully taking this course, students will have a theoretical and practical understanding of:
* The principles behind fundamental software verification techniques, including Hoare-style axiomatic semantics, abstract interpretation, model checking, and testing.
* Application of the principles to the construction of verification tools, in particular program provers.
* Research challenges in these areas.
InhaltThe idea of software verification has been around for decades, but only recently have the techniques become mature enough to be implemented and be applicable in practice. Progress has been made possible by the convergence of different techniques, originally developed in isolation.

This course embraces this diversity of approaches, by surveying some of the main ideas, techniques, and results in software verification. These include in particular:


* Axiomatic semantics, which provides a foundation of program correctness proofs by supplying a rigorous semantics of programs.
* Abstract interpretation, which provides a general framework to express and design static techniques for program analysis.
* Model checking, which provides efficient techniques for the exhaustive exploration of state-based models of programs and reactive systems.
* Testing, which provides the counterpart to exhaustive techniques by defining dynamic analyses to detect programming mistakes and correct them.

To demonstrate some of the techniques in practice, the course will offer a practical project requiring the application of verification tools to illustrative examples.
LiteraturAxiomatic semantics:

* Michael Huth and Mark Ryan. Logic in Computer Science: Modelling and Reasoning about Systems, second edition. Cambridge University Press, 2004
* Aaron Bradley and Zohar Manna. The Calculus of Computation. Springer, 2007.
* David Gries. The Science of Programming. Springer, 1981.
* Bertrand Meyer. Introduction to the Theory of Programming Languages. Prentice Hall, 1990.
* Flemming Nielson and Hanne Riis Nielson. Semantics with Applications: An Appetizer. Springer, 2007.
* Krzysztof R. Apt, Frank S. de Boer, Ernst-Rüdiger Olderog. Verification of Sequential and Concurrent Programs. Springer, 2009.


Abstract interpretation:

* Flemming Nielson, Hanne Riis Nielson, Chris Hankin: Principles of Program Analysis, Springer, ISBN 3-540-65410-0.
* Neil D. Jones, Flemming Nielson: Abstract Interpretation: a Semantic-Based Tool for Program Analysis


Model checking and real-time:

* Edmund M. Clarke, Orna Grumberg, and Doron A. Peled. Model Checking. MIT Press, 2000.
* Carlo A. Furia, Dino Mandrioli, Angelo Morzenti, and Matteo Rossi. Modeling Time in Computing. Monographs in Theoretical Computer Science. An EATCS series. Springer, 2012.


Testing:

* Mauro Pezzè and Michal Young: Software Testing and Analysis: Process, Principles and Techniques, Wiley, 2007.
* Paul Ammann and Jeff Offutt: Introduction to Software Testing, Cambridge University Press, 2008.
252-0273-01LDistributed Software Engineering Laboratory Information
Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.
W8 KP2V + 2U + 3AB. Meyer, P. Kolb, D. M. Nordio
KurzbeschreibungThe Distributed Software Engineering Laboratory introduces the software engineering principles and techniques appropriate for the increasingly prevalent style of modern software development, involving teams spread across teams, companies and countries.

The course involves a distributed project conducted in cooperation with student teams from other universities.
LernzielModern software development is increasingly *distributed*: projects are developed by different groups collaborating across teams, companies, countries, timezones. This setup radically alters the assumptions underlying many of the traditional views of software engineering.

The Distributed Software Engineering Laboratory introduces the principles and techniques for this new paradigm. In line with the "distributed" nature of the topic, the project is performed in collaboration with student teams from other universities in various countries.This course provides students with a clear view of distributed software development, enabling them to participate successfully in distributed projects, and also helping them to devise their own career strategies in the context of the continued trend towards outsourcing.
InhaltBasics of distributed development

The outsourcing phenomenon; country review.

Requirements engineering for distributed projects

Quality assurance for distributed projects.

Process models (especially CMMI) and agile methods

Supplier assessment and qualification.

Negotiating a contract for a distributed project.

Software project management for distributed projects.

Role of interfaces and other technical issues of distributed development.


A key part of the Laboratory is the course project, performed in groups involving teams from other universities. Students get to practice distributed developmemt directly, experiencing issues and applying techniques presented in the course.

The exercise sessions usually start at 9am.
SkriptThe course page includes the full set of slides and links to supplementary documentation.
Voraussetzungen / BesonderesPrerequisites: Basic understanding of programming.
252-0293-00LWireless and Mobile Computing for Entertainment Applications Information W4 KP2V + 1US. Mangold
KurzbeschreibungThis course gives a detailed overview about the 802 standards and summarizes the state of the art for WLANs, WPANs, and WMANs, including new topics such as mesh networks, cognitive radio, and visible light communications. The course combines lectures with a set of assignments in which students are asked to work with a simple JAVA simulation software.
LernzielThe objective of the course is to learn about the general principles of wireless communications, including physics, frequency spectrum regulation, and standards. Further, the most up-to-date standards and protocols used for wireless LAN IEEE 802.11, Bluetooth and Wi-Fi, mesh networks, sensor networks, cellular networks, visible light communication, and cognitive radios, are analyzed and evaluated. Students develop their own add-on mobile computing algorithms to improve the behavior of the systems, using a Java-based event-driven simulator. We also hand out embedded systems that can be used for experiments for optical communication.
InhaltWireless Communication, Wi-Fi, Bluetooth, ZigBee, Standards, Regulation, Algorithms, Radio Spectrum, Cognitive Radio, Mesh Networks, Optical Communication, Visible Light Communication
SkriptThe script will be made available from the course webpage.
Literatur(1) The course blog at http://blogs.ethz.ch/stefanmangold/
(2) The course webpage at http://www.lst.inf.ethz.ch/teaching/lectures/hs14/293/index.html
(3) The JAVA simulation kernel "jemula"
(4) The JAVA 802 protocol emulator "JEmula802"
(5) WALKE, B. AND MANGOLD, S. AND BERLEMANN, L. (2006) IEEE 802 Wireless Systems Protocols, Multi-Hop Mesh/Relaying, Performance and Spectrum Coexistence. New York U.S.A.: John Wiley & Sons. Nov 2006.
(6) BERLEMANN, L. AND MANGOLD, S. (2009) Cognitive Radio for Dynamic Spectrum Access . New York U.S.A.: John Wiley & Sons. Jan 2009.
(7) MANGOLD, S. ET.AL. (2003) Analysis of IEEE 802.11e for QoS Support in Wireless LANs. IEEE Wireless Communications, vol 10 (6), 40-50.
Voraussetzungen / BesonderesStudents should have interest in wireless communication, and should be familiar with JAVA programming.
252-0341-01LInformation Retrieval Information W4 KP2V + 1UT. Hofmann
KurzbeschreibungIntroduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
LernzielIn depth understanding of managing, indexing, and retrieving documents with text, image and XML content. Knowledge about basic search algorithms on the web, benchmarking of search algorithms, and relevance feedback methods.
252-0373-00LMobile and Personal Information Systems Information W4 KP2V + 1UM. Norrie, M. Nebeling
KurzbeschreibungThe course examines how traditional information systems development techniques have been adapted to support various forms of mobile information systems. Topics to be covered include: databases of mobile objects; development of mobile applications; desktop-to-mobile adaptation of user interfaces; context-awareness; mobile vs. personal and social context.
LernzielStudents will acquire an understanding of why and how traditional data management, application development and user interface design techniques have been adapted for mobile information systems.
InhaltAdvances in mobile devices and communication technologies have led to a rapid increase in demands for various forms of mobile information systems where the users, the applications and the databases themselves may be mobile. Based on both lectures and breakout sessions, this course examines the impact of the different forms of mobility and collaboration that systems require nowadays and how these influence the design of systems at the database, the application and the user interface level. For example, traditional data management techniques have to be adapted to meet the requirements of such systems and cope with new connection, access and synchronisation issues. As mobile devices have increasingly become integrated into the users' lives and are expected to support a range of activities in different environments, applications should be context-aware, adapting functionality, information delivery and the user interfaces to the current environment and task. Various forms of software and hardware sensors may be used to determine the current context, raising interesting issues for discussion. Finally, user mobility, and the varying and intermittent connectivity that it implies, gives rise to new forms of dynamic collaboration that require lightweight, but flexible, mechanisms for information synchronisation and consistency maintenance. Here, the interplay of mobile, personal and social context will receive special attention.
252-0417-00LRandomized Algorithms and Probabilistic MethodsW7 KP3V + 2U + 1AA. Steger, A. Ferber
KurzbeschreibungLas Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks
LernzielAfter this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas.
InhaltRandomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas.
SkriptYes.
Literatur- Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)
- Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)
252-0437-00LVerteilte Algorithmen Information W4 KP3VF. Mattern
KurzbeschreibungModelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate.
LernzielKennenlernen von Modellen und Algorithmen verteilter Systeme.
InhaltVerteilte Algorithmen sind Verfahren, die dadurch charakterisiert sind, dass mehrere autonome Prozesse gleichzeitig Teile eines gemeinsamen Problems in kooperativer Weise bearbeiten und der dabei erforderliche Informationsaustausch ausschliesslich über Nachrichten erfolgt. Derartige Algorithmen kommen im Rahmen verteilter Systeme zum Einsatz, bei denen kein gemeinsamer Speicher existiert und die Übertragungszeit von Nachrichten i.a. nicht vernachlässigt werden kann. Da dabei kein Prozess eine aktuelle konsistente Sicht des globalen Zustands besitzt, führt dies zu interessanten Problemen.
Im einzelnen werden u.a. folgende Themen behandelt:
Modelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate.
Literatur- F. Mattern: Verteilte Basisalgorithmen, Springer-Verlag
- G. Tel: Topics in Distributed Algorithms, Cambridge University Press
- G. Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition
- A.D. Kshemkalyani, M. Singhal: Distributed Computing, Cambridge University Press
- N. Lynch: Distributed Algorithms, Morgan Kaufmann Publ
252-0463-00LSecurity Engineering Information W5 KP2V + 2UD. Basin
KurzbeschreibungSubject of the class are engineering techniques for developing secure systems. We examine concepts, methods and tools, applied within the different activities of the SW development process to improve security of the system. Topics: security requirements&risk analysis, system modeling&model-based development methods, implementation-level security, and evaluation criteria for secure systems
LernzielSecurity engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.

The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.

Topics covered include

* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems
InhaltSecurity engineering is an evolving discipline that unifies two important areas: software engineering and security. Software Engineering addresses the development and application of methods for systematically developing, operating, and maintaining, complex, high-quality software.
Security, on the other hand, is concerned with assuring and verifying properties of a system that relate to confidentiality, integrity, and availability of data.

The goal of this class is to survey engineering techniques for developing secure systems. We will examine concepts, methods, and tools that can be applied within the different activities of the software development process, in order to improve the security of the resulting systems.

Topics covered include

* security requirements & risk analysis,
* system modeling and model-based development methods,
* implementation-level security, and
* evaluation criteria for the development of secure systems

Modules taught:

1. Introduction
- Introduction of Infsec group and speakers
- Security meets SW engineering: an introduction
- The activities of SW engineering, and where security fits in
- Overview of this class
2. Requirements Engineering: Security Requirements and some Analysis
- overview: functional and non-functional requirements
- use cases, misuse cases, sequence diagrams
- safety and security
- FMEA, FTA, attack trees
3. Modeling in the design activities
- structure, behavior, and data flow
- class diagrams, statecharts
4. Model-driven security for access control (design)
- SecureUML as a language for access control
- Combining Design Modeling Languages with SecureUML
- Semantics, i.e., what does it all mean,
- Generation
- Examples and experience
5. Model-driven security (Part II)
- Continuation of above topics
6. Security patterns (design and implementation)
7. Implementation-level security
- Buffer overflows
- Input checking
- Injection attacks
8. Testing
- overview
- model-based testing
- testing security properties
9. Risk analysis and management 1 (project management)
- "risk": assets, threats, vulnerabilities, risk
- risk assessment: quantitative and qualitative
- safeguards
- generic risk analysis procedure
- The OCTAVE approach
10. Risk analysis: IT baseline protection
- Overview
- Example
11. Evaluation criteria
- CMMI
- systems security engineering CMM
- common criteria
12. Guest lecture
- TBA
Literatur- Ross Anderson: Security Engineering, Wiley, 2001.
- Matt Bishop: Computer Security, Pearson Education, 2003.
- Ian Sommerville: Software Engineering, 6th ed., Addison-Wesley, 2001.
- John Viega, Gary McGraw: Building Secure Software, Addison-Wesley, 2002.
- Further relevant books and journal/conference articles will be announced in the lecture.
Voraussetzungen / BesonderesPrerequisite: Class on Information Security
252-0523-00LComputational Biology Information W6 KP3V + 2UG. H. Gonnet
KurzbeschreibungStudy 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.
LernzielFamiliarize 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.
InhaltThis 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.
252-0527-00LProbabilistic Graphical Models for Image Analysis Information W4 KP3GB. V. McWilliams
KurzbeschreibungThis course will focus on the algorithms for inference and learning with statistical models. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields.

We will use examples from traditional vision problems such as image registration and image segmentation, as well as recent problems such as object recognition.
LernzielStudents will be introduced to probablistic graphical models and will learn how to apply them to problems in image analysis and understanding. The focus will be to study various algorithms for inference and parameter learning.
LiteraturWill be announced during the lecture.
252-0535-00LMachine Learning Information W6 KP3V + 2UJ. M. Buhmann
KurzbeschreibungMachine 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.
LernzielStudents 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.
InhaltThe 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
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. 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 / BesonderesSolid basic knowledge in analysis, statistics and numerical methods for
CSE. Experience in programming for solving the project tasks.
252-0543-01LComputer Graphics Information W6 KP3V + 2UM. Gross, O. Sorkine Hornung
KurzbeschreibungThis course covers some of the fundamental concepts of computer graphics. The two main parts of the class are image synthesis and geometric modeling.
LernzielAt 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.
InhaltThis 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.
Skriptno
Voraussetzungen / BesonderesPrerequisites:
Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, basic programming skills in C-like languages (we use JavaScript for exercises), Visual Computing core course recommended.
252-0546-00LPhysically-Based Simulation in Computer Graphics Information W4 KP2V + 1UB. Solenthaler, B. Thomaszewski
KurzbeschreibungDie Vorlesung gibt eine Einführung in das Gebiet der physikalisch basierten Animation in der Computer Graphik und einen Überblick über fundamentale Methoden und Algorithmen. In den praktischen Übungen werden drei Aufgabenblätter in kleinen Gruppen bearbeitet. Zudem sollen in einem Programmierprojekt die Vorlesungsinhalte in einem 3D Spiel oder einer vergleichbaren Anwendung umgesetzt werden.
LernzielDie Vorlesung gibt eine Einführung in das Gebiet der physikalisch basierten Animation in der Computer Graphik und einen Überblick über fundamentale Methoden und Algorithmen. In den praktischen Übungen werden drei Aufgabenblätter in kleinen Gruppen bearbeitet. Zudem sollen in einem Programmierprojekt die Vorlesungsinhalte in einem 3D Spiel oder einer vergleichbaren Anwendung umgesetzt werden.
InhaltIn der Vorlesung werden Themen aus dem Gebiet der physikalisch-basierten Modellierung wie Partikel-Systeme, Feder-Masse Modelle, die Methoden der Finiten Differenzen und der Finiten Elemente behandelt. Diese Methoden und Techniken werden verwendet um deformierbare Objekte oder Flüssigkeiten zu simulieren mit Anwendungen in Animationsfilmen, 3D Computerspielen oder medizinischen Systemen. Es werden auch Themen wie Starrkörperdynamik, Kollisionsdetektion und Charakteranimation behandelt.
Voraussetzungen / BesonderesBasiskenntnisse in Analysis und Physik, Algorithmen und Datenstrukturen und der Programmierung in C++. Kenntnisse auf den Gebieten Numerische Mathematik sowie Gewoehnliche und Partielle Differentialgleichungen sind von Vorteil, werden aber nicht vorausgesetzt.
252-1407-00LAlgorithmic Game Theory Information W7 KP3V + 2U + 1AP. Widmayer
KurzbeschreibungGame theory provides a formal model to study the behavior and interaction of self-interested users and programs in large-scale distributed computer systems without central control. The course discusses algorithmic aspects of game theory.
LernzielLearning the basic concepts of game theory and mechanism design, acquiring the computational paradigm of self-interested agents, and using these concepts in the computational and algorithmic setting.
InhaltThe Internet is a typical example of a large-scale distributed computer system without central control, with users that are typically only interested in their own good. For instance, they are interested in getting high bandwidth for themselves, but don't care about others, and the same is true for computational load or download rates. Game theory provides a particularly well-suited model for the behaviour and interaction of such selfish users and programs. Classical game theory dates back to the 1930s and typically does not consider algorithmic aspects at all. Only a few years back, algorithms and game theory have been considered together, in an attempt to reconcile selfish behavior of independent agents with the common good.

This course discusses algorithmic aspects of game-theoretic models, with a focus on recent algorithmic and mathematical developments. Rather than giving an overview of such developments, the course aims to study selected important topics in depth.

Outline:
- Introduction to classical game theoretic concepts.
- Existence of stable solutions (equilibria), algorithms for computing equilibria, computational complexity.
- The cost difference between an optimum under central control and an equilibrium under selfish agents, known as the "price of anarchy".
- Auction-like mechanisms and algorithms that "direct" the actions of selfish agents into a certain desired equilibrium situation.
- Selected current research topics of Algorithmic Game Theory, such as Web-Search Based Keyword Auctions, or Information Cascading in Social Networks
SkriptNo lecture notes.
Literatur"Algorithmic Game Theory", edited by N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani, Cambridge University Press, 2008;

"Game Theory and Strategy", Philip D. Straffin, The Mathematical Association of America, 5th printing, 2004

Several copies of both books are available in the Computer Science library.
Voraussetzungen / BesonderesAudience: Although this is a Computer Science course, we encourage the participation from all students who are interested in this topic.

Requirements: You should enjoy precise mathematical reasoning. You need to have passed a course on algorithms and complexity. No knowledge of game theory is required.
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