# Search result: Catalogue data in Autumn Semester 2014

Certificate of Advanced Studies in Computer Science | ||||||

Focus Courses and Electives | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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252-0237-00L | Concepts of Object-Oriented Programming | W | 6 credits | 3V + 2U | P. Müller | |

Abstract | Course 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 | |||||

Objective | After 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. | |||||

Content | The 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 | |||||

Literature | Will be announced in the lecture. | |||||

Prerequisites / Notice | Prerequisites: Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience | |||||

252-0239-00L | Software Verification | W | 6 credits | 3V + 2U | B. Meyer, C. A. Furia, S. Nanz | |

Abstract | This course surveys some of the main approaches to software verification, including axiomatic semantics, abstract interpretation, model checking, and testing. | |||||

Objective | After 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. | |||||

Content | The 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. | |||||

Literature | Axiomatic 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. | |||||

252-0273-01L | Distributed Software Engineering Laboratory In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum. | W | 8 credits | 2V + 2U + 3A | B. Meyer, P. Kolb, D. M. Nordio | |

Abstract | The 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. | |||||

Objective | Modern 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. | |||||

Content | Basics 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. | |||||

Lecture notes | The course page includes the full set of slides and links to supplementary documentation. | |||||

Prerequisites / Notice | Prerequisites: Basic understanding of programming. | |||||

252-0293-00L | Wireless and Mobile Computing for Entertainment Applications | W | 4 credits | 2V + 1U | S. Mangold | |

Abstract | This 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. | |||||

Objective | The 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. | |||||

Content | Wireless Communication, Wi-Fi, Bluetooth, ZigBee, Standards, Regulation, Algorithms, Radio Spectrum, Cognitive Radio, Mesh Networks, Optical Communication, Visible Light Communication | |||||

Lecture notes | The script will be made available from the course webpage. | |||||

Literature | (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. | |||||

Prerequisites / Notice | Students should have interest in wireless communication, and should be familiar with JAVA programming. | |||||

252-0341-01L | Information Retrieval | W | 4 credits | 2V + 1U | T. Hofmann | |

Abstract | Introduction 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. | |||||

Objective | In 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. | |||||

252-0373-00L | Mobile and Personal Information Systems | W | 4 credits | 2V + 1U | M. Norrie, M. Nebeling | |

Abstract | The 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. | |||||

Objective | Students 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. | |||||

Content | Advances 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. | |||||

252-0417-00L | Randomized Algorithms and Probabilistic Methods | W | 7 credits | 3V + 2U + 1A | A. Steger, A. Ferber | |

Abstract | Las 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 | |||||

Objective | After 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. | |||||

Content | Randomized 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. | |||||

Lecture notes | Yes. | |||||

Literature | - Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995) - Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005) | |||||

252-0437-00L | Distributed Algorithms | W | 4 credits | 3V | F. Mattern | |

Abstract | Models of distributed computations, time space diagrams, virtual time, logical clocks and causality, wave algorithms, parallel and distributed graph traversal, consistent snapshots, mutual exclusion, election and symmetry breaking, distributed termination detection, garbage collection in distributed systems, monitoring distributed systems, global predicates. | |||||

Objective | Become acquainted with models and algorithms for distributed systems. | |||||

Content | Verteilte 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. | |||||

Literature | - 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 | |||||

252-0463-00L | Security Engineering | W | 5 credits | 2V + 2U | D. Basin | |

Abstract | Subject 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 | |||||

Objective | Security 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 | |||||

Content | Security 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 | |||||

Literature | - 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. | |||||

Prerequisites / Notice | Prerequisite: Class on Information Security | |||||

252-0523-00L | Computational Biology | W | 6 credits | 3V + 2U | G. H. Gonnet | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

252-0527-00L | Probabilistic Graphical Models for Image Analysis | W | 4 credits | 3G | B. V. McWilliams | |

Abstract | This 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. | |||||

Objective | Students 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. | |||||

Literature | Will be announced during the lecture. | |||||

252-0535-00L | Machine Learning | W | 6 credits | 3V + 2U | J. M. Buhmann | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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 | |||||

Lecture notes | No lecture notes, but slides will be made available on the course webpage. | |||||

Literature | 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. | |||||

Prerequisites / Notice | 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 credits | 3V + 2U | M. Gross, O. Sorkine Hornung | |

Abstract | This course covers some of the fundamental concepts of computer graphics. The two main parts of the class are image synthesis and geometric modeling. | |||||

Objective | 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. | |||||

Content | 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. | |||||

Lecture notes | no | |||||

Prerequisites / Notice | Prerequisites: 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. | |||||

252-0546-00L | Physically-Based Simulation in Computer Graphics | W | 4 credits | 2V + 1U | B. Solenthaler, B. Thomaszewski | |

Abstract | This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application. | |||||

Objective | This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application. | |||||

Content | The lecture covers topics in physically-based modeling, such as particle systems, mass-spring models, finite difference and finite element methods. These approaches are used to represent and simulate deformable objects or fluids with applications in animated movies, 3D games and medical systems. Furthermore, the lecture covers topics such as rigid body dynamics, collision detection, and character animation. | |||||

Prerequisites / Notice | Fundamentals of calculus and physics, basic concepts of algorithms and data structures, basic programming skills in C++. Knowledge on numerical mathematics as well as ordinary and partial differential equations is an asset, but not required. | |||||

252-1407-00L | Algorithmic Game Theory | W | 7 credits | 3V + 2U + 1A | P. Widmayer | |

Abstract | Game 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. | |||||

Objective | Learning 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. | |||||

Content | The 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 | |||||

Lecture notes | No lecture notes. | |||||

Literature | "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. | |||||

Prerequisites / Notice | Audience: 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. | |||||

252-1411-00L | Security of Wireless Networks | W | 5 credits | 2V + 1U + 1A | S. Capkun, C. Soriente | |

Abstract | Core Elements: Wireless communication channel, Wireless network architectures and protocols, Attacks on wireless networks, Protection techniques. | |||||

Objective | After this course, the students should be able to: describe and classify security goals and attacks in wireless networks; describe security architectures of the following wireless systems and networks: 802.11, GSM/UMTS, RFID, ad hoc/sensor networks; reason about security protocols for wireless network; implement mechanisms to secure 802.11 networks. | |||||

Content | Wireless channel basics. Wireless electronic warfare: jamming and target tracking. Basic security protocols in cellular, WLAN and multi-hop networks. Recent advances in security of multi-hop networks; RFID privacy challenges and solutions. | |||||

252-1414-00L | System Security | W | 5 credits | 2V + 2U | S. Capkun, A. Perrig | |

Abstract | The first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems, such as host based intrusion detection systems. In the second part, the focus is on system design and methodologies for large projects. | |||||

Objective | In this lecture, students learn about the security requirements and capabilities that are expected from modern hardware, operating systems and other software environments. An overview of available technologies, algorithms and standards is given, with which these requirements can be met. | |||||

Content | The first part of the lecture covers individual system's aspects starting with tamperproof or tamperresistant hardware in general over operating system related security mechanisms to application software systems such as host based intrusion detetction systems. The main topics covered are: tamper resistant hardware, CPU support for security, protection mechanisms in the kernel, file system security (permissions / ACLs / network filesystem issues), IPC Security, mechanisms in more modern OS, such as Capabilities and Zones, Libraries and Software tools for security assurance, etc. In the second part, the focus is on system design and methodologies for large projects. The main question answered is how to get a large secure system. Topics include: patch management, common software faults (buffer overflows, etc.), writing secure software (design, architecture, QA, testing), compiler-supported security, langauge-supported security (java...), logging and auditing (BSM audit, dtrace, ...), cryptographic support, TCG, secure file systems, dos/windows/ windowsXP security issues. Along the lectures, model cases will be elaborated and evaluated in the exercises. | |||||

252-1425-00L | Geometry: Combinatorics and Algorithms | W | 6 credits | 2V + 2U + 1A | B. Gärtner, M. Hoffmann, E. Welzl | |

Abstract | Geometric structures are useful in many areas, and there is a need to understand their structural properties, and to work with them algorithmically. The lecture addresses theoretical foundations concerning geometric structures. Central objects of interest are triangulations. We study combinatorial (Does a certain object exist?) and algorithmic questions (Can we find a certain object efficiently?) | |||||

Objective | The goal is to make students familiar with fundamental concepts, techniques and results in combinatorial and computational geometry, so as to enable them to model, analyze, and solve theoretical and practical problems in the area and in various application domains. In particular, we want to prepare students for conducting independent research, for instance, within the scope of a thesis project. | |||||

Content | Planar and geometric graphs, embeddings and their representation (Whitney's Theorem, canonical orderings, DCEL), polygon triangulations and the art gallery theorem, convexity in R^d, planar convex hull algorithms (Jarvis Wrap, Graham Scan, Chan's Algorithm), point set triangulations, Delaunay triangulations (Lawson flips, lifting map, randomized incremental construction), Voronoi diagrams, the Crossing Lemma and incidence bounds, line arrangements (duality, Zone Theorem, ham-sandwich cuts), 3-SUM hardness, counting planar triangulations. | |||||

Lecture notes | yes | |||||

Literature | Mark de Berg, Marc van Kreveld, Mark Overmars, Otfried Cheong, Computational Geometry: Algorithms and Applications, Springer, 3rd ed., 2008. Satyan Devadoss, Joseph O'Rourke, Discrete and Computational Geometry, Princeton University Press, 2011. Stefan Felsner, Geometric Graphs and Arrangements: Some Chapters from Combinatorial Geometry, Teubner, 2004. Jiri Matousek, Lectures on Discrete Geometry, Springer, 2002. Takao Nishizeki, Md. Saidur Rahman, Planar Graph Drawing, World Scientific, 2004. | |||||

Prerequisites / Notice | Prerequisites: The course assumes basic knowledge of discrete mathematics and algorithms, as supplied in the first semesters of Bachelor Studies at ETH. Outlook: In the following spring semester there is a seminar "Geometry: Combinatorics and Algorithms" that builds on this course. There are ample possibilities for Semester-, Bachelor- and Master Thesis projects in the area. | |||||

252-5703-00L | Multimedia Communications | W | 4 credits | 2V + 1U | A. Smolic | |

Abstract | After a summary of fundamentals in signal processing and information theory, an introduction to processing and coding of different types of multimedia is given. | |||||

Objective | Understanding principles of multimedia communications and getting an illustrative overview of available and emerging technology. | |||||

Content | After a summary of fundamentals in signal processing and information theory, an introduction to processing and coding of different types of multimedia is given. This starts with speech (PCM, vocoder, CELP etc.), continues over audio (MP3, AAC etc.), still images (JPEG etc.), video (MPEG-2, MPEG-4, H.264/AVC etc.), and interactive graphics (VRML, MPEG-4), to emerging and future multimedia content such as 3D video and free viewpoint video. Algorithms as well as human perception will be adressed. Content - Fundamentals of information theory - Fundamentals of signal processing and coding - Speech processing and coding - Audio processing and coding - Still image processing and coding - Video processing and Coding - Interactive graphics representation, coding and streaming - Emerging multimedia (3D video, free viewpoint video) | |||||

263-2800-00L | Design of Parallel and High-Performance Computing | W | 7 credits | 3V + 2U + 1A | T. Hoefler, M. Püschel | |

Abstract | Advanced topics in parallel / concurrent programming. | |||||

Objective | Understand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large concurrent software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore. |

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