Search result: Catalogue data in Spring Semester 2020

Electrical Engineering and Information Technology Master Information
Master Studies (Programme Regulations 2008)
Major Courses
A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval.
Computers and Networks
Recommended Subjects
These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.
NumberTitleTypeECTSHoursLecturers
101-0178-01LUncertainty Quantification in Engineering Information W3 credits2GS. Marelli
AbstractUncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis.
ObjectiveAfter this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory.
ContentThe course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (www.uqlab.com).
Lecture notesDetailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.
Prerequisites / NoticeA basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course.

A good knowledge of Matlab is required to participate in the tutorials and for the mini-project.
227-0126-00LAdvanced Topics in Networked Embedded SystemsW2 credits1SL. Thiele, J. Beutel
AbstractThe seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems, internet of things, and sensor networks in various application domains.
ObjectiveThe 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. In addition, participants will improve their presentation, reading and reviewing skills.
ContentThe 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. In particular, they review all presented papers using a standard scientific reviewing system, they present one of the papers orally and they lead the corresponding discussion session.
227-0420-00LInformation Theory II Information
Does not take place this semester.
W6 credits2V + 2UA. Lapidoth
AbstractThis course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
ObjectiveThe course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory.
ContentDifferential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem.
Lecture notesn/a
LiteratureT.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
227-0436-00LDigital Communication and Signal ProcessingW6 credits2V + 2UA. Wittneben
AbstractA comprehensive presentation of modern digital modulation, detection and synchronization schemes and relevant aspects of signal processing enables the student to analyze, simulate, implement and research the physical layer of advanced digital communication schemes. The course both covers the underlying theory and provides problem solving and hands-on experience.
ObjectiveDigital communication systems are characterized by ever increasing requirements on data rate, spectral efficiency and reliability. Due to the huge advances in very large scale integration (VLSI) we are now able to implement extremely complex digital signal processing algorithms to meet these challenges. As a result the physical layer (PHY) of digital communication systems has become the dominant function in most state-of-the-art system designs. In this course we discuss the major elements of PHY implementations in a rigorous theoretical fashion and present important practical examples to illustrate the application of the theory. In Part I we treat discrete time linear adaptive filters, which are a core component to handle multiuser and intersymbol interference in time-variant channels. Part II is a seminar block, in which the students develop their analytical and experimental (simulation) problem solving skills. After a review of major aspects of wireless communication we discuss, simulate and present the performance of novel cooperative and adaptive multiuser wireless communication systems. As part of this seminar each students has to give a 15 minute presentation and actively attends the presentations of the classmates. In Part III we cover parameter estimation and synchronization. Based on the classical discrete detection and estimation theory we develop maximum likelihood inspired digital algorithms for symbol timing and frequency synchronization.
ContentPart I: Linear adaptive filters for digital communication
• Finite impulse response (FIR) filter for temporal and spectral shaping
• Wiener filters
• Method of steepest descent
• Least mean square adaptive filters

Part II: Seminar block on cooperative wireless communication
• review of the basic concepts of wireless communication
• multiuser amplify&forward relaying
• performance evaluation of adaptive A&F relaying schemes and student presentations

Part III: Parameter estimation and synchronization
• Discrete detection theory
• Discrete estimation theory
• Synthesis of synchronization algorithms
• Frequency estimation
• Timing adjustment by interpolation
Lecture notesLecture notes.
Literature[1] Oppenheim, A. V., Schafer, R. W., "Discrete-time signal processing", Prentice-Hall, ISBN 0-13-754920-2.
[2] Haykin, S., "Adaptive filter theory", Prentice-Hall, ISBN 0-13-090126-1.
[3] Van Trees, H. L., "Detection , estimation and modulation theory", John Wiley&Sons, ISBN 0-471-09517-6.
[4] Meyr, H., Moeneclaey, M., Fechtel, S. A., "Digital communication receivers: synchronization, channel estimation and signal processing", John Wiley&Sons, ISBN 0-471-50275-8.
Prerequisites / NoticeFormal prerequisites: none
Recommended: Communication Systems or equivalent
227-0559-00LSeminar in Deep Reinforcement Learning Information Restricted registration - show details
Number of participants limited to 25.
W2 credits2SR. Wattenhofer, O. Richter
AbstractIn this seminar participating students present and discuss recent research papers in the area of deep reinforcement learning. The seminar starts with two introductory lessons introducing the basic concepts. Alongside the seminar a programming challenge is posed in which students can take part to improve their grade.
ObjectiveSince Google Deepmind presented the Deep Q-Network (DQN) algorithm in 2015 that could play Atari-2600 games at a superhuman level, the field of deep reinforcement learning gained a lot of traction. It sparked media attention with AlphaGo and AlphaZero and is one of the most prominent research areas. Yet many research papers in the area come from one of two sources: Google Deepmind or OpenAI. In this seminar we aim at giving the students an in depth view on the current advances in the area by discussing recent papers as well as discussing current issues and difficulties surrounding deep reinforcement learning.
ContentTwo introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: www.disco.ethz.ch/courses.html
Lecture notesSlides of presentations will be made available.
LiteratureOpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers.
The paper selection can be found on www.disco.ethz.ch/courses.html.
Prerequisites / NoticeIt is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.
252-0408-00LCryptographic Protocols Information W6 credits2V + 2U + 1AM. Hirt, U. Maurer
AbstractThe 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.
ObjectiveIndroduction to a very active research area with many gems and paradoxical
results. Spark interest in fundamental problems.
ContentThe 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 notesthe 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 / NoticeA basic understanding of fundamental cryptographic concepts
(as taught for example in the course Information Security or
in the course Cryptography Foundations) is useful, but not required.
851-0734-00LInformation Security Law
Does not take place this semester.
Particularly suitable for students of D-INFK, D-ITET
W2 credits2V
AbstractIntroduction to Information Security Law for non-legal students respectively prospective decision-makers in companies and public authorities who will have to deal with information security issues (CIOs, COOs, CEOs). The lectures will focus on the legal aspects of the security of ICT infrastructures, including networks (Internet), and of the transported and processed information.
ObjectiveThe objective is to understand the meaning and aims of information security and the legal framework, to become acquainted with legal instruments available to provide effective protection for infrastructures and sensitive legal assets and to present an analysis of possible legal loopholes and potential measures. No prior legal knowledge is required for those wishing to attend these lectures.
ContentThe lectures will deal with industry-specific as well as cross-sector specific themes involving both technology and law from the areas of data protection law, computer crimes, statutory duties of confidentiality, telecommunication surveillance (Internet), electronic signatures, liability etc.
Lecture notesThe lectures will be accompanied by powerpoint slide presentations, downloadable before the lectures begin, or available as hard copy at the lectures themselves.
LiteratureReferences to further literature sources will be given in the lectures.
227-0559-10LSeminar in Communication Networks: Learning, Reasoning and Control Restricted registration - show details
Does not take place this semester.
Number of participants limited to 24.
W2 credits2SL. Vanbever, A. Singla
AbstractIn this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. This semester the seminar will focus on topics blending networks with machine learning and control theory.
ObjectiveThe two main goals of this seminar are: 1) learning how to read and review scientific papers; and 2) learning how to present and discuss technical topics with an audience of peers.

Students are required to attend the entire seminar, choose a paper to present from a given list, prepare and give a presentation on that topic, and lead the follow-up discussion. To ensure the talks' quality, each student will be mentored by a teaching assistant. In addition to presenting one paper, every student is also required to submit one (short) review for one of the two papers presented every week in-class (12 reviews in total).

The students will be evaluated based on their submitted reviews, their presentation, their leadership in animating the discussion for their own paper, and their participation in the discussions of other papers.
ContentThe seminar will start with two introductory lectures in week 1 and week 2. Starting from week 3, participating students will start reviewing, presenting, and discussing research papers. Each week will see two presentations, for a total of 24 papers.

The course content will vary from semester to semester. This semester, the seminar will focus on topics blending networks with machine learning and control theory. For details, please see: https://seminar-net.ethz.ch
Lecture notesThe slides of each presentation will be made available on the website.
LiteratureThe paper selection will be made available on the course website: https://seminar-net.ethz.ch
Prerequisites / NoticeCommunication Networks (227-0120-00L) or equivalents. It is expected that students have prior knowledge in machine learning and control theory, for instance by having attended appropriate courses.
252-0312-00LUbiquitous Computing Information W4 credits2V + 1AC. Holz, F. Mattern, S. Mayer
AbstractUnlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses.
Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication.
ObjectiveUnlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses.
Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication.
Lecture notesCopies of slides will be made available
LiteratureWill 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
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