Roger Wattenhofer: Catalogue data in Spring Semester 2020
|Name||Prof. Dr. Roger Wattenhofer|
Inst. f. Techn. Informatik u. K.
ETH Zürich, ETZ G 96
|Telephone||+41 44 632 63 12|
|Department||Information Technology and Electrical Engineering|
|227-0558-00L||Principles of Distributed Computing||7 credits||2V + 2U + 2A||R. Wattenhofer, M. Ghaffari|
|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.)|
|227-0559-00L||Seminar in Deep Reinforcement Learning |
Number of participants limited to 25.
|2 credits||2S||R. Wattenhofer, O. Richter|
|Abstract||In 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.|
|Objective||Since 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.|
|Content||Two 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 notes||Slides of presentations will be made available.|
|Literature||OpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers.|
The paper selection can be found on www.disco.ethz.ch/courses.html.
|Prerequisites / Notice||It is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.|
|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.
|10 credits||9P||G. Alonso, T. Hoefler, F. Mattern, A. Singla, R. Wattenhofer, C. Zhang|
|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.