Niao He: Catalogue data in Autumn Semester 2022

Name Prof. Dr. Niao He
FieldComputer Science
Address
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/
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
252-0945-15LDoctoral Seminar Machine Learning (HS22)
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 credits1SN. He, T. Hofmann, A. Krause, G. Rätsch, M. Sachan
AbstractAn 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.
ObjectiveThe 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.
Prerequisites / NoticeThis 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.
252-5051-00LAdvanced Topics in Machine Learning Information Restricted registration - show details
Number of participants limited to 40.

The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SJ. M. Buhmann, R. Cotterell, N. He, F. Yang, M. El-Assady
AbstractIn this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.
ObjectiveThe seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.
ContentThe seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models.
LiteratureThe papers will be presented in the first session of the seminar.
263-5255-00LFoundations of Reinforcement Learning Information Restricted registration - show details
Does not take place this semester.
Number of participants limited to 190.

The course will be offered again in FS23.
5 credits2V + 2AN. He
AbstractReinforcement 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.
ObjectiveThis 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.
ContentBasic 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.
Lecture notesLecture notes will be posted on Moodle.
LiteratureDynamic 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.
Prerequisites / NoticeStudents are expected to have strong mathematical background in linear algebra, probability theory, optimization, and machine learning.
263-5255-10LFoundations of Reinforcement Learning (Only Assignments)
Does not take place this semester.
Only for Ph.D. students!

Will be offered again in FS23!
2 credits4AN. He
Abstract
Objective
ContentThis 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.