Search result: Catalogue data in Spring Semester 2015
|Computer Science Master|
|263-0008-00L||Computational Intelligence Lab|
Office 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 in Computational Science|
|Focus Core Courses Computational Science|
|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|
|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|
|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.|
|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|
|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
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|
|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|
|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.|
The actual paper selection can be found on www.disco.ethz.ch/courses.html.
|Focus Courses in Information Security|
|Focus Core Courses Information Security|
|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.|
|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|
|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|
|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.|
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