# Search result: Catalogue data in Spring Semester 2015

Computer Science Master | ||||||

Interfocus Courses | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|

263-0008-00L | Computational Intelligence LabOffice hour always on Mondays from 11-12 in room CAB H53 | O | 6 credits | 2V + 2U + 1A | T. Hofmann | |

Abstract | This laboratory course teaches fundamental concepts in computational science and machine learning based on matrix factorization. This method provides a powerful framework of numerical linear algebra that encompasses many important techniques, such as dimension reduction, clustering, combinatorial optimization and sparse coding. | |||||

Objective | Students acquire the fundamental theoretical concepts related to a class of problems that can be solved by matrix factorization. Furthermore, they successfully develop solutions to application problems by following the paradigm of modeling - algorithm development - implementation - experimental validation. This lab course has a strong focus on practical assignments. Students work in groups of two to three people, to develop solutions to three application problems: 1. Compression: Exploiting image statistics to compress an image with minimal perceptual loss. 2. Collaborative filtering: predicting a user interest, based on his own and other peoples ratings. The "Netflix prize" is one such example. 3. Inpainting: Filling in lost parts of an image based on its surroundings. For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper. | |||||

Focus Courses | ||||||

Focus Courses in Computational Science | ||||||

Focus Core Courses Computational Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

263-2300-00L | How To Write Fast Numerical Code Prerequisite: Master student, solid C programming skills. | W | 6 credits | 3V + 2U | M. Püschel | |

Abstract | This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning. | |||||

Objective | Software performance (i.e., runtime) arises through the interaction of algorithm, its implementation, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this interaction, and hence software performance, focusing on numerical or mathematical functionality. The second goal is to teach a general systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in a few homeworks and semester-long group projects. | |||||

Content | The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research. Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning. | |||||

Focus Elective Courses Computational Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0526-00L | Statistical Learning Theory | W | 4 credits | 2V + 1U | J. M. Buhmann | |

Abstract | The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models. | |||||

Objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed. | |||||

Content | # Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification. # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error? # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future. | |||||

Lecture notes | no script; transparencies of the lectures will be made available. | |||||

Literature | Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000. Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||

Prerequisites / Notice | Requirements: basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course. | |||||

151-0104-00L | Uncertainty Quantification for Engineering & Life Sciences Does not take place this semester. Number of participants limited to 40. | W | 4 credits | 3G | P. Koumoutsakos | |

Abstract | Quantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology. | |||||

Objective | The course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures. | |||||

Content | Topics that will be covered include: Uncertainty quantification under parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis. | |||||

Lecture notes | The class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class. | |||||

Literature | 1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia 2. Probability Theory: The Logic of Science by E. T. Jaynes 3. Class Notes | |||||

Prerequisites / Notice | Fundamentals of Probability, Fundamentals of Computational Modeling | |||||

Seminar Computational Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-5251-00L | Computational Science | W | 2 credits | 2S | P. Arbenz, T. Hoefler, P. Koumoutsakos | |

Abstract | Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory. | |||||

Objective | Studying and presenting fundamental works of Computational Science. Learning how to make a scientific presentation. | |||||

Content | Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory. | |||||

Lecture notes | none | |||||

Literature | Papers will be distributed in the first seminar in the first week of the semester | |||||

252-5704-00L | Advanced Methods in Computer Graphics Number of participants limited to 24. | W | 2 credits | 2S | M. Gross, O. Sorkine Hornung | |

Abstract | This 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. | |||||

Objective | The 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. | |||||

Focus Courses in Distributed Systems | ||||||

Focus Core Courses Distributed Systems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

227-0558-00L | Principles of Distributed Computing | W | 6 credits | 2V + 2U + 1A | R. Wattenhofer | |

Abstract | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||

Objective | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||

Content | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||

Lecture notes | Available. Our course script is used at dozens of other universities around the world. | |||||

Literature | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||

Prerequisites / Notice | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||

Focus Elective Courses Distributed Systems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0312-00L | Ubiquitous Computing | W | 3 credits | 2V | F. Mattern | |

Abstract | Ubiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact. | |||||

Objective | The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact. | |||||

Lecture notes | Copies of slides will be made available | |||||

Literature | Will be provided in the lecture. To put you in the mood: Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104 | |||||

252-0807-00L | Information Systems Laboratory Number of participants limited to 16. In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. These Labs will only count towards the Master Programme. Additional Labs will be listed on the Addendum. | W | 10 credits | 9P | M. Norrie | |

Abstract | The purpose of this laboratory course is to practically explore modern techniques to build large-scale distributed information systems. Participants will work in groups of three or more students, and develop projects in several phases. | |||||

Objective | The students will gain experience of working with technologies used in the design and development of information systems. | |||||

Content | First week: Kick-off meeting and project assignment Second week: Meeting with the project supervisor to discuss the goals and scope of the project. During the semester: Individual group work. Each team member should contribute to the project roughly about 10h/week, excluding any necessary reading or self-studying (e.g. the time spent to learn a new technology). In addition, it is expected that each team can meet with their supervisor on a regular basis. End of semester: Final presentation. | |||||

252-0817-00L | Distributed Systems 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 | 10 credits | 9P | G. Alonso, F. Mattern, T. Roscoe, R. Wattenhofer | |

Abstract | This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. | |||||

Objective | Students acquire practical knowledge about technologies from the area of distributed systems. | |||||

Content | This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. The objecte of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course. For information of the course or projects available, please contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso. | |||||

263-3501-00L | Advanced Computer Networks | W | 5 credits | 2V + 2U | T. Roscoe, P. M. Stüdi | |

Abstract | This course covers a set of advanced topics in computer networks. The focus is on principles, architectures, and protocols used in modern networked systems, such as the Internet itself, wireless and mobile networks, and large-scale peer-to-peer systems. | |||||

Objective | The goals of the course is to build on basic networking course material in providing an understanding of the tradeoffs and existing technology in building large, complex networked systems, and provide concrete experience of the challenges through a series of lab exercises. | |||||

Content | The focus of the course is on principles, architectures, and protocols used in modern networked systems. Topics include: wireless networks and mobility issues at the network and transport layer (Mobile IP and micromobility protocols, TCP in wireless environments). Mobile phone networks. Overlay networks, flat routing protocols (DHTs), and peer-to-peer architectures. The Border Gateway Protocol (BGP) in practice. | |||||

263-3700-00L | User Interface Engineering | W | 4 credits | 2V + 1U | O. Hilliges | |

Abstract | An in-depth introduction to the core concepts of post-desktop user interface engineering. Current topics in UI research, in particular non-desktop based interaction, mobile device interaction, augmented and mixed reality, and advanced sensor and output technologies. | |||||

Objective | Students will learn about fundamental aspects pertaining to the design and implementation of modern (non-desktop) user interfaces. Students will understand the basics of human cognition and capabilities as well as gain an overview of technologies for input and output of data. The core competency acquired through this course is a solid foundation in data-driven algorithms to process and interpret human input into computing systems. At the end of the course students should be able to understand and apply advanced hardware and software technologies to sense and interpret user input. Students will be able to develop systems that incorporate non-standard sensor and display technologies and will be able to apply data-driven algorithms in order to extract semantic meaning from raw sensor data. | |||||

Content | User Interface Engineering covers theoretical and practical aspects relating to the design and implementation of modern non-standard user interfaces. A particular area of interest are machine-learning based algorithms for input recognition in advanced non-desktop user interfaces, including UIs for mobile devices but also Augmented Reality UIs, gesture and multi-modal user interfaces. The course covers three main areas: I) Basic principles of human cognition and perception (and their application for UIs) II) (Hardware) technologies for user input sensing III) Data-driven methods for input recognition (gestures, speech, etc.) Specific topics include: * Model Human Processor (MHP) model - prediction of task completion times. * Fitts' Law - measure of information load on human motor and cognitive system during user interaction. * Touch sensor technologies (capacitive, resistive, force sensing etc). * Data-driven algorithms for user input recognition: - SVMs for classification and regression - Randomized Decision Forests for gesture recognition and pose estimation - Markov chains and HMMs for gesture and speech recognition - Optical flow and other image processing and computer vision techniques - Input filtering (Kalman) * Applications of the above in HCI research | |||||

Lecture notes | Slides and other materials will be available online. Lecture slides on a particular topic will typically not be made available prior the completion of that lecture. | |||||

Literature | A detailed reading list will be made available on the course website. | |||||

Prerequisites / Notice | Prerequisites: proficiency in a programming language such as C, programming methodology, problem analysis, program structure, etc. Normally met through an introductory course in programming in C, C++, Java. The following courses are strongly recommended as prerequisite: * "Human Computer Interaction" * "Machine Learning" * "Visual Computing" or "Computer Vision" The course will be assessed by a written Midterm and Final examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises. | |||||

Seminar in Distributed Systems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-3600-02L | Ubiquitous Computing Seminar | W | 2 credits | 2S | F. Mattern, O. Hilliges | |

Abstract | Seminar on various topics from the broader areas of Pervasive Computing, Ubiquitous Computing, Human Computer Interaction, and Distributed Systems. | |||||

Objective | Learn about various current topics from the broader areas of Pervasive Computing, Ubiquitous Computing, Human Computer Interaction, and Distributed Systems. | |||||

Prerequisites / Notice | There will be an orientation event several weeks before the start of the semester (possibly at the end of the preceding semester) where also first topics will be assigned to students. Please check http://www.vs.inf.ethz.ch/edu for further information. | |||||

263-3830-00L | Software Defined Networking: The Data Centre Perspective | W | 2 credits | 2S | T. Roscoe | |

Abstract | Software Defined Networks (SDN) is a change supported not only by research but also industry and redifens how traditional network management and configuration is been done. | |||||

Objective | Through review and discussion of literature on an exciting new trend in networking, the students get the opportunity to get familiar with one of the most promising new developments in data centre connectivity, while at the same time they can develop soft skills related to the evaluation and presentation of professional content. | |||||

Content | Software Defined Networks (SDN) is a change supported not only by research but also industry and redifens how traditional network management and configuration is been done. Although much has been already investigated and there are already functional SDN-enabled switches there are many open questions ahead of the adoption of SDN inside and outside the data centre (traditional or cloud-based). With a series of seminars we will reflect on the challenges, adoption strategies and future trends of SDN to create an understanding how SDN is affecting the network operators' industry. | |||||

Literature | The seminar is based on recent publications by academia and industry. Links to the publications are placed on the Seminar page and can be downloaded from any location with access to the ETH campus network. | |||||

Prerequisites / Notice | The seminar bases on active and interactive participation of the students. | |||||

227-0126-00L | Advanced Topics in Networked Embedded Systems Number of participants limited to 12. | W | 2 credits | 1S | O. Saukh, J. Beutel, L. Thiele | |

Abstract | The seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems and sensor networks in various application domains. | |||||

Objective | The goal is to get a deeper understanding on leading edge technologies in the discipline, on classes of applications, and on current as well as future research directions. | |||||

Content | The seminar enables Master students, PhDs and Postdocs to learn about latest breakthroughs in wireless sensor networks, networked embedded systems and devices, and energy-harvesting in several application domains, including environmental monitoring, tracking, smart buildings and control. Participants are requested to actively participate in the organization and preparation of the seminar. | |||||

227-0559-00L | Seminar in Distributed Computing | W | 2 credits | 2S | R. Wattenhofer | |

Abstract | In this seminar participating students present and discuss recent research papers in the area of distributed computing. The seminar consists of algorithmic as well as systems papers in distributed computing theory, peer-to-peer computing, ad hoc and sensor networking, or multi-core computing. | |||||

Objective | In the last two decades, we have experienced an unprecedented growth in the area of distributed systems and networks; distributed computing now encompasses many of the activities occurring in today's computer and communications world. This course introduces the basics of distributed computing, highlighting common themes and techniques. We study the fundamental issues underlying the design of distributed systems: communication, coordination, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. In this seminar, students present the latest work in this domain. Seminar language: English | |||||

Content | Different each year. For details see: www.disco.ethz.ch/courses.html | |||||

Lecture notes | Slides of presentations will be made available. | |||||

Literature | Papers. The actual paper selection can be found on www.disco.ethz.ch/courses.html. | |||||

Focus Courses in Information Security | ||||||

Focus Core Courses Information Security | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0407-00L | Cryptography | W | 7 credits | 3V + 2U + 1A | U. Maurer | |

Abstract | Fundamentals and applications of cryptography. Cryptography as a mathematical discipline: reductions, constructive cryptography paradigm, security proofs. The discussed primitives include cryptographic functions, pseudo-randomness, symmetric encryption and authentication, public-key encryption, key agreement, and digital signature schemes. Selected cryptanalytic techniques. | |||||

Objective | The goals are: (1) understand the basic theoretical concepts and scientific thinking in cryptography; (2) understand and apply some core cryptographic techniques and security proof methods; (3) be prepared and motivated to access the scientific literature and attend specialized courses in cryptography. | |||||

Content | See course description. | |||||

Lecture notes | yes. | |||||

Prerequisites / Notice | Familiarity with the basic cryptographic concepts as treated for example in the course "Information Security" is required but can in principle also be acquired in parallel to attending the course. | |||||

Focus Elective Courses Information Security | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0408-00L | Cryptographic Protocols | W | 5 credits | 2V + 2U | U. Maurer, M. Hirt | |

Abstract | The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc. | |||||

Objective | Indroduction to a very active research area with many gems and paradoxical results. Spark interest in fundamental problems. | |||||

Content | The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc. | |||||

Lecture notes | the lecture notes are in German, but they are not required as the entire course material is documented also in other course material (in english). | |||||

Prerequisites / Notice | A basic understanding of fundamental cryptographic concepts (as taught for example in the course Information Security or in the course Cryptography) is useful, but not required. | |||||

263-4600-00L | Formal Methods for Information Security | W | 4 credits | 2V + 1U | S. Radomirovic, M. Torabi Dashti | |

Abstract | The course focuses on formal methods for the modelling and analysis of security and privacy concerns in critical systems, ranging from access control policies to cryptographic protocols. | |||||

Objective | The students will learn the key ideas and theoretical foundations of formal modelling and analysis of security protocols and policies. The students will complement their theoretical knowledge by solving practical exercises and using various related tools. | |||||

Content | The lecture treats formal methods for the modelling and analysis of security-critical systems. The first part of the lecture focuses on access control policies in centralized and distributed settings. Access control policies are an integral part of modern Internet services; examples include single sign-on endpoints, distributed trust management in social Websites, and peer-to-peer networks. The lectures cover the formal foundations of authorization systems, and their applications to the synthesis and analysis of access control policies. We will also study a few notable existing models, such as XACML, DKAL and PBel. The second part of the lecture concentrates on cryptographic protocols. Cryptographic protocols (such as SSL/TLS, SSH, Kerberos, SAML single-sign on, and IPSec) form the basis for secure communication and business processes. Numerous attacks on published protocols show that the design of cryptographic protocols is extremely error-prone. A rigorous analysis of these protocols is therefore indispensable. The lecture covers the theoretical basis for the formal modeling and analysis of such protocols. Specifically, we discuss their operational semantics, the formalization of security properties, and techniques and algorithms for their verification. In addition to the classical security properties for confidentiality and authentication, we will study privacy properties and the fairness property in contract signing. The accompanying tutorials provide an opportunity to apply the theory and tools to concrete protocols. | |||||

Seminar in Information Security | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-4800-00L | Quantum Information and Cryptography | W | 2 credits | 2S | S. Wolf | |

Abstract | In this advanced seminar, various topics are treated in the intersection of quantum physics, information theory, and cryptography. | |||||

Objective | see above | |||||

Focus Courses in Information Systems | ||||||

Focus Core Courses Information Systems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0374-00L | Web Engineering | W | 6 credits | 2V + 2U + 1A | M. Norrie | |

Abstract | The course teaches students about the basic principles of web engineering by examining the various technologies used in modern web sites in detail together with the step-by-step processes used to develop state-of-the art web sites. | |||||

Objective | The goals of the course are that students should be able to: - systematically develop state-of-the-art web sites using a range of technologies, platforms and frameworks in common use - understand the role of different technologies and how they are combined in practice - analyse requirements and select appropriate technologies, platforms and frameworks | |||||

Content | The first half of the course will introduce the various technologies used in state-of-the-art web sites together with the step-by-step development process. From the beginning, we will cater for access from multiple devices such as mobile phones and tablets as well as desktop browsers and show how technologies such as HTML5, CSS3 and JavaScript can be used to support rich forms of interaction. In the second half of the course, we will look at how various platforms and frameworks are used to support web site development. We will start by examining the model behind modern content management platforms such as WordPress and showing how web sites with dynamic content can be systematically developed using these platforms. This will be followed by looking at the more traditional programming approaches by first introducing the Java web technology stack and then a modern web application framework. Finally, we will present model-driven approaches to web engineering. The material covered in lectures will be supported by a series of practical exercises that will take the students through the development processes. | |||||

Focus Elective Courses Information Systems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0312-00L | Ubiquitous Computing | W | 3 credits | 2V | F. Mattern | |

Abstract | Ubiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact. | |||||

Objective | The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact. | |||||

Lecture notes | Copies of slides will be made available | |||||

Literature | Will be provided in the lecture. To put you in the mood: Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104 | |||||

252-0355-00L | Object Databases | W | 4 credits | 2V + 1U | A. K. de Spindler | |

Abstract | The course examines the principles and techniques of providing data management in object-oriented programming environments. After introducing the basics of object storage and management, we will cover semantic object models and their implementation. Finally, we discuss advanced data management services such as version models for temporal and engineering databases and for software configuration. | |||||

Objective | The goal of this course is to extend the student's knowledge of database technologies towards object-oriented solutions. Starting with basic principles, students also learn about commercial products and research projects in the domain of object-oriented data management. Apart from getting to know the characteristics of these approaches and the differences between them, the course also discusses what application requirements justify the use of object-oriented databases. Therefore, it educates students to make informed decisions on when to use what database technology. | |||||

Content | The course examines the principles and techniques of providing data management in object-oriented programming environments. It is divided into three parts that cover the road from simple object persistence, to object-oriented database management systems and to advanced data management services. In the first part, object serialisation and object-relational mapping frameworks will be introduced. Using the example of the open-source project db4o, the utilisation, architecture and functionality of a simple object-oriented database is discussed. The second part of the course is dedicated to advanced topics such as industry standards and solutions for object data management as well as storage and index technologies. Additionally, advanced data management services such as version models for temporal and engineering databases as well as for software configuration are discussed. In the third and last part of the course, an object-oriented data model that features a clear separation of typing and classification is presented. Together with the model, its implementation in terms of an object-oriented database management system is discussed also. Finally, an extension of this data model is presented that allows context-aware data to be managed. | |||||

Prerequisites / Notice | Prerequisites: Knowledge about the topics of the lectures "Introduction to Databases" and "Information Systems" is required. | |||||

252-0807-00L | Information Systems Laboratory Number of participants limited to 16. In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. These Labs will only count towards the Master Programme. Additional Labs will be listed on the Addendum. | W | 10 credits | 9P | M. Norrie | |

Abstract | The purpose of this laboratory course is to practically explore modern techniques to build large-scale distributed information systems. Participants will work in groups of three or more students, and develop projects in several phases. | |||||

Objective | The students will gain experience of working with technologies used in the design and development of information systems. | |||||

Content | First week: Kick-off meeting and project assignment Second week: Meeting with the project supervisor to discuss the goals and scope of the project. During the semester: Individual group work. Each team member should contribute to the project roughly about 10h/week, excluding any necessary reading or self-studying (e.g. the time spent to learn a new technology). In addition, it is expected that each team can meet with their supervisor on a regular basis. End of semester: Final presentation. | |||||

252-3005-00L | Introduction to Natural Language Processing | W | 4 credits | 2V + 1U | E. Alfonseca Cubero, M. Ciaramita | |

Abstract | This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||

Objective | The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques. | |||||

Content | This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. | |||||

Literature | Lectures will be presented from the Jurafsky and Martin text accompanied by related technical papers where necessary. | |||||

263-5200-00L | Data Mining: Learning from Large Data Sets Does not take place this semester. The course will be offered again in the autumn semester 2015. | W | 4 credits | 2V + 1U | A. Krause | |

Abstract | Many scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. | |||||

Objective | Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, we will study principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course will both cover theoretical foundations and practical applications. | |||||

Content | Topics covered: - Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk) - Fast nearest neighbor methods (Shingling, locality sensitive hashing) - Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines) - Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback) - Active learning (uncertainty sampling, pool-based methods, label complexity) - Dimension reduction (random projections, nonlinear methods) - Data streams (Sketches, coresets, applications to online clustering) - Recommender systems | |||||

Prerequisites / Notice | Prerequisites: Solid basic knowledge in statistics, algorithms and programming. Background in machine learning is helpful but not required. | |||||

Seminar in Information Systems | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-3002-00L | Algorithms for Database Systems | W | 2 credits | 2S | P. Widmayer, A. Khan | |

Abstract | Query processing, optimization, stream-based systems, distributed and parallel databases, non-standard databases. | |||||

Objective | Develop an understanding of selected problems of current interest in the area of algorithms for database systems. | |||||

252-3100-00L | Computer Supported Cooperative Work Number of participants limited to 18. | W | 2 credits | 2S | M. Norrie | |

Abstract | Computer-Supported Cooperative Work (CSCW) is the study of how people work together using computer technology. It is a multi-disciplinary research field dealing with the social, theoretical, practical and technical aspects of collaboration and how the use of technology can affect groups, organisations and communities. The diversity of the CSCW field is reflected in the range of topics covered. | |||||

Objective | Computer-Supported Cooperative Work (CSCW) is the study of how people work together using computer technology. It is a multi-disciplinary research field dealing with the social, theoretical, practical and technical aspects of collaboration and how the use of technology can affect groups, organisations, communities and societies. The CSCW community is interested in how people use everyday tools such as email, the web and chat systems as well as specialist groupware applications that support groups of people engaged in shared tasks such as software development or product design. A better understanding of how people communicate and work together can in turn lead to a better understanding of the problems of current technologies and systems and influence the design of new technologies and tools. | |||||

Focus Courses in Software Engineering | ||||||

Focus Core Courses Software Engineering In spring 2015 there will be no course offered in this category. | ||||||

Focus Elective Courses Software Engineering | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0268-00L | Concepts of Concurrent Computation | W | 7 credits | 3V + 2U + 1A | S. Nanz | |

Abstract | Concurrent programming is one of the major challenges in software development. The "Concepts of Concurrent Computation" course explores important models of concurrency, with a special emphasis on concurrent object-oriented programming and process calculi. | |||||

Objective | After completing this course, students will understand the principles and techniques of concurrent programming, supporting theories allowing formal reasoning about concurrent systems, and advances in concurrent object-oriented programming. | |||||

Content | Topics include: Overview - Concurrent and parallel programming - Multitasking and multiprocessing - Shared-memory and distributed-memory multiprocessing - Notion of process and thread - Performance of concurrent systems Approaches to concurrent programming - Issues: data races, deadlock, starvation - Synchronization algorithms - Semaphores - Monitors - Java and .NET multithreading Concurrent object-oriented programming: the SCOOP model - Processors; handling an object - Synchronous and asynchronous feature calls - Design by Contract in a concurrent context - Separate objects and entities - Accessing separate objects; validity rules - Synchronization: waiting, reserving, preconditions as wait conditions, Wait by Necessity - Examples and applications Programming approaches to concurrency - Message-passing vs. shared-memory communication - Language examples: Ada, Polyphonic C#, Erlang (Actors), X10, Linda, Cilk and others. - Lock-free programming - Software Transactional Memory Reasoning about concurrent programs - Properties of concurrent programs - Temporal logic - Process calculi: CCS and coalgebra - Petri nets - Proofs of concurrent programs | |||||

Literature | - Bertrand Meyer and Sebastian Nanz: Course textbook (draft) - Mordechai Ben-Ari: Principles of Concurrent and Distributed Programming. Prentice Hall, 2006 - Maurice Herlihy and Nir Shavit: The Art of Multiprocessor Programming. Morgan Kaufmann, 2008 - Gregory R. Andrews: Foundations of Multithreaded, Parallel, and Distributed Programming. Addison Wesley, 1999 | |||||

Prerequisites / Notice | The course's lectures are of two different kinds: the Tuesday session is a traditional lecture; the Wednesday session is devoted to seminar talks by the student participants, based on research papers related to the topics of the course. The research papers to be presented will be assigned at the start of the course. | |||||

263-2300-00L | How To Write Fast Numerical Code Prerequisite: Master student, solid C programming skills. | W | 6 credits | 3V + 2U | M. Püschel | |

Abstract | This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning. | |||||

Objective | Software performance (i.e., runtime) arises through the interaction of algorithm, its implementation, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this interaction, and hence software performance, focusing on numerical or mathematical functionality. The second goal is to teach a general systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in a few homeworks and semester-long group projects. | |||||

Content | The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research. Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning. | |||||

263-2810-00L | Advanced Compiler Design | W | 7 credits | 3V + 2U + 1A | T. Gross | |

Abstract | This course covers advanced topics in compiler design: SSA intermediate representation and its use in optimization, just-in-time compilation, profile-based compilation, exception handling in modern programming languages. | |||||

Objective | Understand translation of object-oriented programs, opportunities and difficulties in optimizing programs using state-of-the-art techniques (profile-based compilation, just-in-time compilation, runtime system interaction) | |||||

Content | This course builds conceptually on Compiler Design (a basic class for advanced undergraduates), but this class is not a prerequisite. Students should however have a solid understanding of basic compiler technology. The focus is on handling the key features of modern object-oriented programs. We review implementations of single and multiple inheritance (incl. object layout, method dispatch) and optimization opportunities. Specific topics: intermediate representations (IR) for optimizing compilers, static single assignment (SSA) representation, constant folding, partial redundancy optimizations, profiling, profile-guided code generation. Special topics as time permits: debugging optimized code, multi-threading, data races, object races, memory consistency models, programming language design. Review of single inheritance, multiple inheritance, object layout, method dispatch, type analysis, type propagation and related topics. This course provides another opportunity to explore software design in a medium-scale software project. | |||||

Literature | Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition). In addition, papers as provided in the class. | |||||

Prerequisites / Notice | A basic course on compiler design is helpful but not mandatory. Student should have programming skills/experience to implement an optimizer (or significant parts of an optimizer) for a simple object-oriented language. The programming project is implemented using Java. | |||||

263-2910-00L | Program Analysis | W | 4 credits | 2V + 1U | M. Vechev | |

Abstract | Modern program analysis techniques are the predominant approach for automatically reasoning about real world programs -- its techniques have been applied in a vast range of application domains. The course provides an introduction to the fundamental principles, applications, and research trends of modern program analysis. | |||||

Objective | The course has 4 main objectives: * Understand the foundational principles behind program analysis techniques. * Understand how to apply these principles to build practical, working analyzers for real world problems. * Understand how to combine these techniques with other approaches (e.g. machine learning techniques) to build powerful end-to-end reasoning systems, not possible otherwise. * Gain familiarity with the state-of-the-art in the area and the future research trends in the next 5-10 years. | |||||

Content | The last decade has seen an explosion in modern program analysis techniques. These techniques are increasingly being used to reason about a vast range of computational paradigms including: * finding security violations in web and mobile applications such as JavaScript and Android * practical type checking and inference (e.g. Facebook's recently released Flow analyzer). * combinations with machine learning techniques for learning from massive programming data guiding prediction of program properties and prediction of new code. * establishing properties of biological systems (e.g. DNA computation) * finding serious errors in systems software (e.g. Linux kernel, device drivers, file systems) * automatic discovery of new algorithms (e.g. concurrent data structures, distributed algorithms) and end-user programming. * compilers for domain specific languages * architecture-driven reasoning of concurrent software (e.g. Intel's x86, ARM, IBM's Power). This course will provide a comprehensive introduction to modern, state-of-the-art program analysis concepts, principles and research trends, including: * Static & Dynamic Analysis: - concepts: memory safety, type checking and inference, typestate, concurrency analysis, abstract interpretation (domains, soundness, precision, fixed points) - frameworks: Valgrind, FastTrack, EventRacer, Apron, PPL, Facebook's Flow analyzer. * Statistical program reasoning: - concepts: combining analysis with statistical models (e.g. Language models, Bayesian networks, Neural networks, etc) - frameworks: Slang, JSNice (http://jsnice.org) * Predicate abstraction: - concepts: Graf-Saidi, Boolean programs, lazy abstraction - frameworks: Microsoft's SLAM for C programs, Fender * Symbolic execution: - concepts: SMT, concolic execution - frameworks: S2E, KLEE, Sage * Security Analysis: - concepts: static + dynamic combination - example: malware detection * Pointer analysis: - concepts: Andersen's, Steensgaard's analysis - frameworks: Soot, LLVM, WALA * Program synthesis: - concepts: L*, version spaces, PBE, CEGIS - frameworks: Sketch, AGS, SmartEdit, ReSynth * Applications of Analysis & Synthesis: - GPU programs, security errors, device drivers, concurrent algorithms, end-user programming. To gain a deeper understanding of how to apply these techniques in practice, the course will involve a small hands-on programming project where based on the principles introduced in class, the students will build a program reasoning engine (e.g. analysis, predictions) for a modern programming language. | |||||

Lecture notes | The lectures notes will be distributed in class. | |||||

Literature | Distributed in class. | |||||

Prerequisites / Notice | This course is aimed at both graduate (M.Sc., PhD) students as well as advanced undergraduate students. | |||||

252-0284-00L | Java and C # in depth Does not take place this semester. | W | 5 credits | 2V + 1U + 1A | to be announced | |

Abstract | Java and C#, both similar and each with its own characteristics, are important languages with wide applications. This course goes into the depth of both languages, each considered for itself but also in comparison with the other. | |||||

Objective | This course provides students with an in-depth understanding of: - The language design philosophy behind Java. - The language design philosophy behind C#. - The key language mechanisms of both languages, and how to use them. - The main properties differentiating the languages. | |||||

Content | Introduction, object-oriented concepts. Frameworks overview and in-the-small language features. Classes, objects, inheritance, polymorphism. Packages/assemblies, abstract classes and interfaces. Exceptions and genericity. Reflection. Threads and Concurrency. Persistence. Web Services. | |||||

Prerequisites / Notice | The course is particularly intended for students already having a knowledge of an object-oriented programming language (one of the two listed, or another one such as Eiffel). | |||||

252-0286-00L | System Construction Does not take place this semester. The course will be offered again in the autumn semester 2015. | W | 4 credits | 2V + 1U | not available | |

Abstract | Main goal is teaching knowledge and skills needed for building custom operating systems and runtime environments. Relevant topics are studied at the example of sufficiently simple systems that have been built at our Institute in the past, ranging from purpose-oriented single processor real-time systems up to generic system kernels on multi-core hardware. | |||||

Objective | The lecture's main goal is teaching of knowledge and skills needed for building custom operating systems and runtime environments. The lecture intends to supplement more abstract views of software construction, and to contribute to a better understanding of "how it really works" behind the scenes. | |||||

Content | Case Study 1: Embedded System - Safety-critical and fault-tolerant monitoring system - Based on an auto-pilot system for helicopters Case Study 2: Multi-Processor Operating System - Universal operating system for symmetric multiprocessors - Shared memory approach - Based on Language-/System Codesign (Active Oberon / A2) Case Study 3: Custom designed Single-Processor System - RISC Single-processor system designed from scratch - Hardware on FPGA - Graphical workstation OS and compiler (Project Oberon) Case Study 4: Custom-designed Multi-Processor System - Special purpose heterogeneous system on a chip - Masssively parallel hard- and software architecture based on message passing - Focus: dataflow based applications | |||||

Lecture notes | Printed lecture notes will be delivered during the lecture. Slides will also be available from the lecture homepage. | |||||

Seminar in Software Engineering | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

263-2100-00L | Research Topics in Software Engineering Number of participants limited to 22. | W | 2 credits | 2S | T. Hoefler | |

Abstract | This seminar introduces students to fundamental results in parallel programming and design. Students will study and present research papers that span topics in both theory and practice, ranging from foundations parallel computing to applications. The focus will be on fundamental lower and upper bounds, thus, many papers will be dated. Students need a solid mathematical background. | |||||

Objective | At the end of the course, the students should be familiar with a broad range of key research results in the area of parallel computing, know how to read and assess papers in the area, and be able to highlight practical examples/applications, limitations of existing work, and outline potential improvements. | |||||

Content | A selection of research papers with a focus on foundations of parallel computing/programming. | |||||

Literature | The publications to be presented will be announced on the seminar home page at least one week before the first session. | |||||

Prerequisites / Notice | Papers will be distributed in the first session. | |||||

Focus Courses in Theoretical Computer Science | ||||||

Focus Core Courses Theoretical Computer Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0407-00L | Cryptography | W | 7 credits | 3V + 2U + 1A | U. Maurer | |

Abstract | Fundamentals and applications of cryptography. Cryptography as a mathematical discipline: reductions, constructive cryptography paradigm, security proofs. The discussed primitives include cryptographic functions, pseudo-randomness, symmetric encryption and authentication, public-key encryption, key agreement, and digital signature schemes. Selected cryptanalytic techniques. | |||||

Objective | The goals are: (1) understand the basic theoretical concepts and scientific thinking in cryptography; (2) understand and apply some core cryptographic techniques and security proof methods; (3) be prepared and motivated to access the scientific literature and attend specialized courses in cryptography. | |||||

Content | See course description. | |||||

Lecture notes | yes. | |||||

Prerequisites / Notice | Familiarity with the basic cryptographic concepts as treated for example in the course "Information Security" is required but can in principle also be acquired in parallel to attending the course. | |||||

252-0491-00L | Satisfiability of Boolean Formulas - Combinatorics and Algorithms | W | 7 credits | 3V + 2U + 1A | E. Welzl | |

Abstract | Basics (CNF, resolution), extremal properties (probabilistic method, derandomization, Local Lemma, partial satisfaction), 2-SAT algorithms (random walk, implication graph), NP-completeness (Cook-Levin), cube (facial structure, Kraft inequality, Hamming balls, covering codes), SAT algorithms (satisfiability coding lemma, Paturi-Pudlák-Zane, Hamming ball search, Schöning), constraint satisfaction. | |||||

Objective | Studying of advanced methods in algorithms design and analysis, and in discrete mathematics along a classical problem in theoretical computer science. | |||||

Content | Satisfiability (SAT) is the problem of deciding whether a boolean formula in propositional logic has an assignment that evaluates to true. SAT occurs as a problem and is a tool in applications (e.g. Artificial Intelligence and circuit design) and it is considered a fundamental problem in theory, since many problems can be naturally reduced to it and it is the 'mother' of NP-complete problems. Therefore, it is widely investigated and has brought forward a rich body of methods and tools, both in theory and practice (including software packages tackling the problem). This course concentrates on the theoretical aspects of the problem. We will treat basic combinatorial properties (employing the probabilistic method including a variant of the Lovasz Local Lemma), recall a proof of the Cook-Levin Theorem of the NP-completeness of SAT, discuss and analyze several deterministic and randomized algorithms and treat the threshold behavior of random formulas. In order to set the methods encountered into a broader context, we will deviate to the more general set-up of constraint satisfaction and to the problem of proper k-coloring of graphs. | |||||

Lecture notes | There exists no book that covers the many facets of the topic. Lecture notes covering the material of the course will be distributed. | |||||

Literature | Here is a list of books with material vaguely related to the course. They can be found in the textbook collection (Lehrbuchsammlung) of the Computer Science Library: George Boole, An Investigation of the Laws of Thought on which are Founded the Mathematical Theories of Logic and Probabilities, Dover Publications (1854, reprinted 1973). Peter Clote, Evangelos Kranakis, Boolean Functions and Computation Models, Texts in Theoretical Computer Science, An EATCS Series, Springer Verlag, Berlin (2002). Nadia Creignou, Sanjeev Khanna, Madhu Sudhan, Complexity Classifications of Boolean Constrained Satisfaction Problems, SIAM Monographs on Discrete Mathematics and Applications, SIAM (2001). Harry R. Lewis, Christos H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall (1998). Rajeev Motwani, Prabhakar Raghavan, Randomized Algorithms, Cambridge University Press, Cambridge, (1995). Uwe Schöning, Logik für Informatiker, BI-Wissenschaftsverlag (1992). Uwe Schöning, Algorithmik, Spektrum Akademischer Verlag, Heidelberg, Berlin (2001). Michael Sipser, Introduction to the Theory of Computation, PWS Publishing Company, Boston (1997). Klaus Truemper, Design of Logic-based Intelligent Systems, Wiley-Interscience, John Wiley & Sons, Inc., Hoboken (2004). | |||||

Prerequisites / Notice | Language: The course will be given in German if nobody expresses preference for English. All accompanying material (lecture notes, web-page, etc.) is supplied in English. Prerequisites: The course assumes basic knowledge in propositional logic, probability theory and discrete mathematics, as it is supplied in the first two years of the Bachelor Studies at ETH. Outlook: There will be a follow-up seminar, SAT, on the topic in the subsequent semester (attendance of this course will be a prerequisite for participation in the seminar). There are ample possibilities for theses of various types (Master-, etc.). | |||||

Focus Elective Courses Theoretical Computer Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0408-00L | Cryptographic Protocols | W | 5 credits | 2V + 2U | U. Maurer, M. Hirt | |

Abstract | The course presents a selection of hot research topics in cryptography. The choice of topics varies and may include provable security, interactive proofs, zero-knowledge protocols, secret sharing, secure multi-party computation, e-voting, etc. | |||||

Objective | Indroduction to a very active research area with many gems and paradoxical results. Spark interest in fundamental problems. | |||||

Content | ||||||

Lecture notes | the lecture notes are in German, but they are not required as the entire course material is documented also in other course material (in english). | |||||

Prerequisites / Notice | A basic understanding of fundamental cryptographic concepts (as taught for example in the course Information Security or in the course Cryptography) is useful, but not required. | |||||

252-1403-00L | Introduction to Quantum Information Processing | W | 3 credits | 2G | S. Wolf | |

Abstract | Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseudo-telepathy", quantum cryptography, as well as the main concepts of quantum information theory. | |||||

Objective | It is the goal of this course to get familiar with the most important notions that are of importance for the connection between Information and Physics. The formalism of Quantum Physics will be motivated and derived, and the use of these laws for information processing will be understood. In particular, the important algorithms of Grover as well as Shor will be studied and analyzed. | |||||

Content | According to Landauer, "information is physical". In quantum information, one is interested in the consequences and the possibilites offered by the laws of quantum physics for information processing. Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated: Quantum algorithms, teleportation, quantum communication complexity and "pseude-telepathy", quantum cryptography, as well as the main concepts of quantum information theory. | |||||

252-1424-00L | Models of Computation | W | 6 credits | 2V + 2U + 1A | M. Cook | |

Abstract | This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more. | |||||

Objective | see above | |||||

Content | This course surveys many different models of computation: Turing Machines, Cellular Automata, Finite State Machines, Graph Automata, Circuits, Tilings, Lambda Calculus, Fractran, Chemical Reaction Networks, Hopfield Networks, String Rewriting Systems, Tag Systems, Diophantine Equations, Register Machines, Primitive Recursive Functions, and more. | |||||

263-4100-00L | Randomized Algorithms and Probabilistic Methods: Advanced Topics | W | 5 credits | 2V + 1U + 1A | J. Lengler, K. Bringmann, T. S. Luria | |

Abstract | Advanced introduction to random walks. | |||||

Objective | Students will learn some advances techniques to analyze random walks, and they will see some examples of how algorithms make use of random walks. To successfully complete this course students need to be able to apply the acquired techniques and use random walks as a tool for designing algorithms. | |||||

Content | The lecture gives an advanced introduction to random walks. We treat methods to analyze them, and applications in which random walks are used. Some open problems will be discussed as well. Topics: - drift analysis - rapidly mixing random walks - random sampling and/or approximate counting e.g. of triangulations, latin squares - expander graphs - volume estimation of convex bodies - differential equation method | |||||

Prerequisites / Notice | Randomized Algorithms and Probabilistic Methods, or a similar course | |||||

401-3052-05L | Graph Theory | W | 5 credits | 2V + 1U | B. Sudakov | |

Abstract | Basic notions, . Trees, spanning trees, Caley formula, Vertex and edge connectivity, blocks, 2-connectivity, Maders theorem, Mengers theorem, Euleraing graphs, Hamilton cycle, Dirac theorem, Matchings, theorem of Hall, Konig, Tutte, Planar graph, Euler's formula, Basic non-planar graphs, Graph colorings, greedy, brooks theorem, 5-colorings of planar graphs | |||||

Objective | The students will get an overview over the most fundamental questions concerning graph theory. We expect them to understand the proof techniques and to use them autonomously on related problems. | |||||

Lecture notes | Lecture will be only at the blackboard. | |||||

Literature | West, D.: "Introduction to Graph Theory" Diestel, R.: "Graph Theory" Further literature links will be provided in the lecture. | |||||

Prerequisites / Notice | NOTICE: This course unit was previously offered as 252-1408-00L Graphs and Algorithms. | |||||

401-3903-11L | Geometric Integer Programming | W | 6 credits | 2V + 1U | R. Weismantel | |

Abstract | Integer programming is the task of minimizing a linear function over all the integer points in a polyhedron. This lecture introduces the key concepts of an algorithmic theory for solving such problems. | |||||

Objective | The purpose of the lecture is to provide a geometric treatment of the theory of integer optimization. | |||||

Content | Key topics are: - lattice theory and the polynomial time solvability of integer optimization problems in fixed dimension, - the theory of integral generating sets and its connection to totally dual integral systems, - finite cutting plane algorithms based on lattices and integral generating sets. | |||||

Lecture notes | not available, blackboard presentation | |||||

Literature | Bertsimas, Weismantel: Optimization over Integers, Dynamic Ideas 2005. Schrijver: Theory of linear and integer programming, Wiley, 1986. | |||||

Prerequisites / Notice | "Mathematical Optimization" (401-3901-00L) | |||||

401-4904-00L | Combinatorial Optimization | W | 6 credits | 2V + 1U | R. Zenklusen | |

Abstract | Combinatorial Optimization deals with efficiently finding a provably strong solution among a finite set of options. This course discusses key combinatorial structures and techniques to design efficient algorithms for combinatorial optimization problems. We put a strong emphasis on polyhedral methods, which proved to be a powerful and unifying tool throughout combinatorial optimization. | |||||

Objective | The goal of this lecture is to get a thorough understanding of various modern combinatorial optimization techniques with an emphasis on polyhedral approaches. Students will learn a general toolbox to tackle a wide range of combinatorial optimization problems. | |||||

Content | Key topics include: - Polyhedral descriptions; - Combinatorial uncrossing; - Ellipsoid method; - Equivalence between separation and optimization; - Design of efficient approximation algorithms for hard problems. | |||||

Lecture notes | Not available. | |||||

Literature | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 5th edition, Springer, 2012. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency, Springer, 2003. This work has 3 volumes. | |||||

Prerequisites / Notice | This course builds upon "Mathematical Optimization" (401-3901-00L), which is a prerequisite for taking this lecture. | |||||

263-4205-00L | Polynomials Does not take place this semester. | W | 4 credits | 2V + 1U | E. Welzl | |

Abstract | Algebraic methods belong among the most powerful and succesful mathematical tools in computer science and discrete mathematics. The course covers a number of results, some of them fairly recent, whose proofs illustrate general techniques. | |||||

Objective | Extending the knowledge of mathematical methods that proved useful in recent research related to theoretical computer science. The students should understand several successful ideas of applying the properties of multivariate polynomials to various problems. | |||||

Content | From the wide area of algebraic methods, we focus mainly on applications of polynomials, and we will encounter some of the elementary concepts of algebraic geometry. Here are some of the main themes: Dimension arguments using spaces of polynomials. Matchings and determinants. Randomized testing of polynomial identities. Space partitions using polynomials and geometric incidence theorems. "Contagious vanishing" arguments, geometry of lines in space. | |||||

Lecture notes | One part of the lecture will follow the book "Thirty-three miniatures" by J. Matousek. The rest will be based on recent research papers and on a book in preparation by Larry Guth. | |||||

Literature | J. Matousek: Thirty-three miniatures, Amer. Math. Soc. 2010 | |||||

Seminar in Theoretical Computer Science | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-3002-00L | Algorithms for Database Systems | W | 2 credits | 2S | P. Widmayer, A. Khan | |

Abstract | Query processing, optimization, stream-based systems, distributed and parallel databases, non-standard databases. | |||||

Objective | Develop an understanding of selected problems of current interest in the area of algorithms for database systems. | |||||

252-4102-00L | Seminar on Randomized Algorithms and Probabilistic Methods | W | 2 credits | 2S | A. Steger | |

Abstract | The aim of the seminar is to study papers which bring the students to the forefront of today's research topics. This semester we will study selected papers of the conference Symposium on Discrete Algorithms (SODA15). | |||||

Objective | Read papers from the forefront of today's research; learn how to give a scientific talk. | |||||

Prerequisites / Notice | The seminar is open for both students from mathematics and students from computer science. As prerequisite we require that you passed the course Randomized Algorithms and Probabilistic Methods (or equivalent, if you come from abroad). | |||||

252-4202-00L | Seminar in Theoretical Computer Science | W | 2 credits | 2S | E. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, B. Sudakov | |

Abstract | Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates. | |||||

Objective | To get an overview of current research in the areas covered by the involved research groups. To present results from the literature. | |||||

252-4302-00L | Seminar Algorithmic Game Theory | W | 2 credits | 2S | P. Widmayer, M. Mihalak | |

Abstract | In the seminar we will get familiar with the current original research in the area of algorithmic game theory by reading and presenting selected research papers in that area. | |||||

Objective | Develop an understanding of selected problems of current interest in the area of algorithmic game theory, and a practice of a scientific presentation. | |||||

Content | Study and understanding of selected topics of current interest in algorithmic game theory such as: Complexity Results (class PPAD, PLS, NP), Sponsored Search, Approximation Algorithms via Algorithmic Game Theory, Price of Anarchy, New paradigms of computation (e.g., envy-fee, truthful), Mechanism Design. | |||||

Literature | Selected research articles. | |||||

Prerequisites / Notice | You must have passed our "Algorithmic Game Theory" class (or have acquired equivalent knowledge, in exceptional cases). | |||||

252-4800-00L | Quantum Information and Cryptography | W | 2 credits | 2S | S. Wolf | |

Abstract | In this advanced seminar, various topics are treated in the intersection of quantum physics, information theory, and cryptography. | |||||

Objective | see above | |||||

263-4203-00L | Geometry: Combinatorics and Algorithms | W | 2 credits | 2S | B. Gärtner, M. Hoffmann, E. Welzl | |

Abstract | This seminar is held once a year and complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. The seminar is a good preparation for a master, diploma, or semester thesis in the area. | |||||

Objective | Each student is expected to read, understand, and elaborate on a selected research paper. To this end, (s)he should give a 45-min. presentation about the paper. The process includes * getting an overview of the related literature; * understanding and working out the background/motivation: why and where are the questions addressed relevant? * understanding the contents of the paper in all details; * selecting parts suitable for the presentation; * presenting the selected parts in such a way that an audience with some basic background in geometry and graph theory can easily understand and appreciate it. | |||||

Prerequisites / Notice | To attend the seminar, some knowledge in (discrete and computational) geometry and graphs and algorithms is required. Thus, previous participation in the course "Geometry: Combinatorics & Algorithms" or a comparable course is strongly encouraged. | |||||

Focus Courses in Visual Computing | ||||||

Focus Core Courses Visual Computing In spring 2015 there will be no course offered in this category. | ||||||

Focus Elective Courses Visual Computing | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0526-00L | Statistical Learning Theory | W | 4 credits | 2V + 1U | J. M. Buhmann | |

Abstract | The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models. | |||||

Objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed. | |||||

Content | # Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification. # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error? # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future. | |||||

Lecture notes | no script; transparencies of the lectures will be made available. | |||||

Literature | Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000. Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||

Prerequisites / Notice | Requirements: basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course. | |||||

252-0538-00L | Shape Modeling and Geometry Processing | W | 4 credits | 2V + 1U | O. Sorkine Hornung, D. Panozzo | |

Abstract | This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry and interactive shape editing. | |||||

Objective | The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing. | |||||

Content | Recent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry and interactive shape editing. | |||||

Lecture notes | Slides and course notes | |||||

Prerequisites / Notice | Prerequisites: Introduction to Computer Graphics, experience with C++ programming. Some background in geometry or computational geometry is helpful, but not necessary. | |||||

252-0570-00L | Game Programming Laboratory In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. These Labs will only count towards the Master Programme. Additional Labs will be listed on the Addendum. | W | 10 credits | 9P | B. Sumner | |

Abstract | The goal of this course is the in-depth understanding of the technology and programming underlying computer games. Students gradually design and develop a computer game in small groups and get acquainted with the art of game programming. | |||||

Objective | The goal of this new course is to acquaint students with the technology and art of programming modern three-dimensional computer games. | |||||

Content | This is a new course that addresses modern three-dimensional computer game technology. During the course, small groups of students will design and develop a computer game. Focus will be put on technical aspects of game development, such as rendering, cinematography, interaction, physics, animation, and AI. In addition, we will cultivate creative thinking for advanced gameplay and visual effects. The "laboratory" format involves a practical, hands-on approach with neither traditional lectures nor exercises. Instead, we will meet once a week to discuss technical issues and to track progress. We plan to utilize Microsoft's XNA Game Studio Express, which is a collection libraries and tools that facilitate game development. While development will take place on PCs, we will ultimately deploy our games on the XBox 360 console. At the end of the course we will present our results to the public. | |||||

Lecture notes | Online XNA documentation. | |||||

Prerequisites / Notice | The number of participants is limited. Prerequisites include: - good programming skills (Java, C++, C#, etc.) - CG experience: Students should have taken, at a minimum, Visual Computing. Higher level courses are recommended, such as Introduction to Computer Graphics, Surface Representations and Geometric Modeling, and Physically-based Simulation in Computer Graphics. | |||||

252-0579-00L | 3D Photography | W | 4 credits | 3G | M. Pollefeys, T. Sattler | |

Abstract | The goal of this course is to provide students with a good understanding of how 3D object shape and appearance can be estimated from images and videos. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises and a course project. | |||||

Objective | After attending this course students should: 1. Understand the concepts that allow recovering 3D shape from images. 2. Have a good overview of the state of the art in 3D photography 3. Be able to critically analyze and asses current research in the area 4. Implement components of a 3D photography system. | |||||

Content | The course will cover the following topics a.o. camera model and calibration, single-view metrology, triangulation, epipolar and multi-view geometry, two-view and multi-view stereo, structured-light, feature tracking and matching, structure-from-motion, shape-from-silhouettes and 3D modeling and applications. | |||||

252-5705-00L | Image Synthesis | W | 6 credits | 5G | W. Jarosz, W. A. Jakob | |

Abstract | This course covers advanced topics in rendering and image synthesis. | |||||

Objective | The goal is to get a broader knowledge of rendering algorithms and an in-depth understanding of advanced topics in rendering. Students will learn about the principles of how light interacts with a scene, and how to translate the associated image formation problem into efficient rendering algorithms. Since this is an upper-level coarse, a focus is placed on state of the art techniques and recent trends in research. | |||||

Content | This course expands upon the rendering foundation taught in the Computer Graphics course. We assume a basic knowledge of ray tracing and shading, and expand significantly on the physics of light transport, discuss the rendering equation, and focus significant time on advanced techniques to enhance the realism and lower the computational cost of rendered images. Starting from a review of the physics underlying a range of complex light transport effects (depth-of-field, soft shadows, global illumination, participating media, subsurface scattering), we discuss how to leverage various mathematical tools (e.g. density estimation, Monte Carlo sampling, Markov Chain Monte Carlo) to obtain a range of state-of-the-art rendering algorithms (including variants of path tracing, photon mapping, and Metropolis light transport). The course includes a rendering competition where students create a realistic image of their choosing using the rendering software they develop in the course. | |||||

Literature | Students will read from the course text books, as well as rendering research papers. | |||||

Prerequisites / Notice | Calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Computer Graphics core course, Visual Computing core course | |||||

263-3700-00L | User Interface Engineering | W | 4 credits | 2V + 1U | O. Hilliges | |

Abstract | An in-depth introduction to the core concepts of post-desktop user interface engineering. Current topics in UI research, in particular non-desktop based interaction, mobile device interaction, augmented and mixed reality, and advanced sensor and output technologies. | |||||

Objective | Students will learn about fundamental aspects pertaining to the design and implementation of modern (non-desktop) user interfaces. Students will understand the basics of human cognition and capabilities as well as gain an overview of technologies for input and output of data. The core competency acquired through this course is a solid foundation in data-driven algorithms to process and interpret human input into computing systems. At the end of the course students should be able to understand and apply advanced hardware and software technologies to sense and interpret user input. Students will be able to develop systems that incorporate non-standard sensor and display technologies and will be able to apply data-driven algorithms in order to extract semantic meaning from raw sensor data. | |||||

Content | User Interface Engineering covers theoretical and practical aspects relating to the design and implementation of modern non-standard user interfaces. A particular area of interest are machine-learning based algorithms for input recognition in advanced non-desktop user interfaces, including UIs for mobile devices but also Augmented Reality UIs, gesture and multi-modal user interfaces. The course covers three main areas: I) Basic principles of human cognition and perception (and their application for UIs) II) (Hardware) technologies for user input sensing III) Data-driven methods for input recognition (gestures, speech, etc.) Specific topics include: * Model Human Processor (MHP) model - prediction of task completion times. * Fitts' Law - measure of information load on human motor and cognitive system during user interaction. * Touch sensor technologies (capacitive, resistive, force sensing etc). * Data-driven algorithms for user input recognition: - SVMs for classification and regression - Randomized Decision Forests for gesture recognition and pose estimation - Markov chains and HMMs for gesture and speech recognition - Optical flow and other image processing and computer vision techniques - Input filtering (Kalman) * Applications of the above in HCI research | |||||

Lecture notes | Slides and other materials will be available online. Lecture slides on a particular topic will typically not be made available prior the completion of that lecture. | |||||

Literature | A detailed reading list will be made available on the course website. | |||||

Prerequisites / Notice | Prerequisites: proficiency in a programming language such as C, programming methodology, problem analysis, program structure, etc. Normally met through an introductory course in programming in C, C++, Java. The following courses are strongly recommended as prerequisite: * "Human Computer Interaction" * "Machine Learning" * "Visual Computing" or "Computer Vision" The course will be assessed by a written Midterm and Final examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises. | |||||

252-5706-00L | Mathematical Foundations of Computer Graphics and Vision | W | 4 credits | 2V + 1U | J.‑C. Bazin, C. Öztireli | |

Abstract | This course presents the fundamental mathematical tools and concepts used in computer graphics and vision. Each theoretical topic is introduced in the context of practical vision or graphic problems, showcasing its importance in real-world applications. | |||||

Objective | The main goal is to equip the students with the key mathematical tools necessary to understand state-of-the-art algorithms in vision and graphics. In addition to the theoretical part, the students will learn how to use these mathematical tools to solve a wide range of practical problems in visual computing. After successfully completing this course, the students will be able to apply these mathematical concepts and tools to practical industrial and academic projects in visual computing. | |||||

Content | The theory of each mathematical concept or tool will be introduced and we will then showcase their practical utility in a variety of different applications in computer graphics and vision. The course will cover topics in sampling, reconstruction, optimization, differentiation, quadrature and spectral methods. Applications will include 3D surface reconstruction, structure from motion, camera pose estimation, image editing, character animation, ray tracing, architectural design and shape recognition. | |||||

227-1034-00L | Computational Vision | W | 6 credits | 2V + 1U | D. Kiper, K. A. Martin | |

Abstract | This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision. | |||||

Objective | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||

Content | This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered. | |||||

Literature | Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995. | |||||

263-5200-00L | Data Mining: Learning from Large Data Sets Does not take place this semester. The course will be offered again in the autumn semester 2015. | W | 4 credits | 2V + 1U | A. Krause | |

Abstract | Many scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. | |||||

Objective | Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, we will study principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course will both cover theoretical foundations and practical applications. | |||||

Content | Topics covered: - Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk) - Fast nearest neighbor methods (Shingling, locality sensitive hashing) - Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines) - Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback) - Active learning (uncertainty sampling, pool-based methods, label complexity) - Dimension reduction (random projections, nonlinear methods) - Data streams (Sketches, coresets, applications to online clustering) - Recommender systems | |||||

Prerequisites / Notice | Prerequisites: Solid basic knowledge in statistics, algorithms and programming. Background in machine learning is helpful but not required. | |||||

Seminar in Visual Computing | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-5704-00L | Advanced Methods in Computer Graphics Number of participants limited to 24. | W | 2 credits | 2S | M. Gross, O. Sorkine Hornung | |

Abstract | This 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. | |||||

Objective | The 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. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0820-00L | Case Studies from Practice | W | 4 credits | 2V + 1U | M. Brandis | |

Abstract | The course is designed to provide students with an understanding of "real-life" challenges from business settings and teach them how to address these. | |||||

Objective | By 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. | |||||

Content | The 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). | |||||

263-0600-00L | Research in Computer Science Only for Computer Science MSc. | W | 5 credits | 11A | Professors | |

Abstract | Independent project work under the supervision of a Computer Science Professor. | |||||

Objective | see above | |||||

Prerequisites / Notice | Only 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. | |||||

272-0300-00L | Algorithmics for Hard Problems This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A. | W | 4 credits | 2V + 1U | J. Hromkovic, H.‑J. Böckenhauer, D. Komm | |

Abstract | This 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. | |||||

Objective | To systematically acquire an overview of the methods for solving hard problems. | |||||

Content | First, 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 notes | Unterlagen und Folien werden zur Verfügung gestellt. | |||||

Literature | J. Hromkovic: Algorithmics for Hard Problems, Springer 2004. R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006. F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010. | |||||

272-0302-00L | Approximation and Online Algorithms | W | 4 credits | 2V + 1U | H.‑J. Böckenhauer, D. Komm | |

Abstract | This lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches. | |||||

Objective | Get 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. | |||||

Content | Approximation 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. | |||||

Literature | Die 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 | |||||

401-3632-00L | Computational Statistics | W | 10 credits | 3V + 2U | M. 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. | |||||

Objective | Getting 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. | |||||

Content | Course 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 http://stat.ethz.ch/education/ . Exercises will be based on the open-source statistics software R (http://www.R-project.org/). Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended. More details are available via the webpage http://stat.ethz.ch/education/ (-> "Computational Statistics"). | |||||

Lecture notes | lecture notes are available online; see http://stat.ethz.ch/education/ (-> "Computational Statistics"). | |||||

Literature | (see the link above, and the lecture notes) | |||||

Prerequisites / Notice | Basic "applied" mathematical calculus and linear algebra. At least one semester of (basic) probability and statistics. | |||||

272-0301-00L | Methods for Design of Random Systems 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. | W | 4 credits | 2V + 1U | J. Hromkovic | |

Abstract | The 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. | |||||

Objective | To understand the computational power of randomness and to learn the basic methods for designing randomized algorithms | |||||

Lecture notes | J. Hromkovic: Randomisierte Algorithmen, Teubner 2004. J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006. J.Hromkovic: Algorithmics for Hard Problems, Springer 2004. | |||||

Literature | J. 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. | ||||||

Internship | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

252-0700-00L | Internship Only for Computer Science MSc. | W | 0 credits | external organisers | ||

Abstract | Internship in a computer science company, which is admitted by the CS Department at ETH. Minimum 10 weeks fulltime employment. | |||||

Objective | The 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 / Notice | A 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 | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

263-0800-00L | Master's Thesis 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. | O | 30 credits | 64D | Professors | |

Abstract | Independent project work supervised by a Computer Science professor. Duration 6 months. | |||||

Objective | To work independently and to produce a scientifically structured work under the supervision of a Computer Science Professor. |