Niao He: Katalogdaten im Herbstsemester 2021

NameFrau Prof. Dr. Niao He
LehrgebietInformatik
Adresse
Professur für Informatik
ETH Zürich, OAT Y 21.1
Andreasstrasse 5
8092 Zürich
SWITZERLAND
E-Mailniao.he@inf.ethz.ch
URLhttps://odi.inf.ethz.ch/
DepartementInformatik
BeziehungAusserordentliche Professorin

NummerTitelECTSUmfangDozierende
252-0945-13LDoctoral Seminar Machine Learning (HS21)
Only for Computer Science Ph.D. students.

This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar.
2 KP1SJ. M. Buhmann, N. He, A. Krause, G. Rätsch, M. Sachan
KurzbeschreibungAn essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
LernzielThe seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills.
Voraussetzungen / BesonderesThis doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab.
263-5255-00LFoundations of Reinforcement Learning Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 190.

Last cancellation/deregistration date for this graded semester performance: Thursday, 28 October 2021! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".
5 KP2V + 2AN. He
KurzbeschreibungReinforcement learning (RL) has been in the limelight of many recent breakthroughs in artificial intelligence. This course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets M.S. students with strong research interests in reinforcement learning, optimization, and control.
LernzielThis course aims to provide students with an advanced introduction of RL theory and algorithms as well as bring them near the frontier of this active research field.

By the end of the course, students will be able to
- Identify the strengths and limitations of various reinforcement learning algorithms;
- Formulate and solve sequential decision-making problems by applying relevant reinforcement learning tools;
- Generalize or discover “new” applications, algorithms, or theories of reinforcement learning towards conducting independent research on the topic.
InhaltBasic topics include fundamentals of Markov decision processes, approximate dynamic programming, linear programming and primal-dual perspectives of RL, model-based and model-free RL, policy gradient and actor-critic algorithms, Markov games and multi-agent RL. If time allows, we will also discuss advanced topics such as batch RL, inverse RL, causal RL, etc. The course keeps strong emphasis on in-depth understanding of the mathematical modeling and theoretical properties of RL algorithms.
SkriptLecture notes will be posted on Moodle.
LiteraturDynamic Programming and Optimal Control, Vol I & II, Dimitris Bertsekas
Reinforcement Learning: An Introduction, Second Edition, Richard Sutton and Andrew Barto.
Algorithms for Reinforcement Learning, Csaba Czepesvári.
Reinforcement Learning: Theory and Algorithms, Alekh Agarwal, Nan Jiang, Sham M. Kakade.
Voraussetzungen / BesonderesStudents are expected to have strong mathematical background in linear algebra, probability theory, optimization, and machine learning.
263-5255-10LFoundations of Reinforcement Learning (Only Assignments)
Only for Ph.D. students! Last cancellation/deregistration date for this graded semester performance: Thursday, 28 October 2021! Please note that after that date no deregistration will be accepted and the course will be considered as "fail".
2 KP4AN. He
Kurzbeschreibung
Lernziel
InhaltThis course focuses on theoretical and algorithmic foundations of reinforcement learning, through the lens of optimization, modern approximation, and learning theory. The course targets students with strong research interests in reinforcement learning, optimization under uncertainty, and data-driven control.