Roger Wattenhofer: Katalogdaten im Frühjahrssemester 2020

Name	Herr Prof. Dr. Roger Wattenhofer
Lehrgebiet	Distributed Computing
Adresse	Inst. f. Techn. Informatik u. K. ETH Zürich, ETZ G 96 Gloriastrasse 35 8092 Zürich SWITZERLAND
Telefon	+41 44 632 63 12
E-Mail	wattenhofer@ethz.ch
URL	http://www.disco.ethz.ch
Departement	Informationstechnologie und Elektrotechnik
Beziehung	Ordentlicher Professor

Nummer	Titel	ECTS	Umfang	Dozierende
227-0558-00L	Principles of Distributed Computing	7 KP	2V + 2U + 2A	R. Wattenhofer, M. Ghaffari
Kurzbeschreibung	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.
Lernziel	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.
Inhalt	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
Skript	Available. Our course script is used at dozens of other universities around the world.
Literatur	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
Voraussetzungen / Besonderes	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 KP	2S	R. Wattenhofer, O. Richter
Kurzbeschreibung	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.
Lernziel	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.
Inhalt	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
Skript	Slides of presentations will be made available.
Literatur	OpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers. The paper selection can be found on www.disco.ethz.ch/courses.html.
Voraussetzungen / Besonderes	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 Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.	10 KP	9P	G. Alonso, T. Hoefler, F. Mattern, A. Singla, R. Wattenhofer, C. Zhang
Kurzbeschreibung	Entwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme. Das Projekt kann aus unterschiedlichen Teilbereichen (von Web-Services bis hin zu ubiquitären Systemen) stammen; typische Technologien umfassen drahtlose Ad-hoc-Netze oder Anwendungen auf Mobiltelefonen.
Lernziel	Erwerb praktischer Kenntnisse bei Entwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme.
Inhalt	Entwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme. Das Projekt kann aus unterschiedlichen Teilbereichen (von Web-Services bis hin zu ubiquitären Systemen) stammen; typische Technologien umfassen drahtlose Ad-hoc-Netze oder Anwendungen auf Mobiltelefonen. Zu diesem Praktikum existiert keine Vorlesung. Bei Interesse bitte einen der beteiligten Professoren oder einen Assistenten der Forschungsgruppen kontaktieren.