Andreas Krause: Catalogue data in Autumn Semester 2021

Name	Prof. Dr. Andreas Krause
Field	Computer Science
Address	Institut für Maschinelles Lernen ETH Zürich, OAT Y 13.1 Andreasstrasse 5 8092 Zürich SWITZERLAND
Telephone	+41 44 632 63 22
Fax	+41 44 623 15 62
E-mail	krausea@ethz.ch
URL	http://las.ethz.ch/krausea.html
Department	Computer Science
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
252-0945-13L	Doctoral 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 credits	1S	J. M. Buhmann, N. He, A. Krause, G. Rätsch, M. Sachan
Abstract	An 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.
Learning objective	The 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 / Notice	This 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.
263-5156-00L	Beyond iid Learning: Causality, Dynamics, and Interactions Number of participants limited to 60. The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.	2 credits	2S	M. Mühlebach, A. Krause, B. Schölkopf
Abstract	Many machine learning problems go beyond supervised learning on independent data points and require an understanding of the underlying causal mechanisms, the interactions between the learning algorithms and their environment, and adaptation to temporal changes. The course highlights some of these challenges and relates them to state-of-the-art research.
Learning objective	The goal of this seminar is to gain experience with machine learning research and foster interdisciplinary thinking.
Content	The seminar will be divided into two parts. The first part summarizes the basics of statistical learning theory, game theory, causal inference, and dynamical systems in four lectures. This sets the stage for the second part, where distinguished speakers will present selected aspects in greater detail and link them to their current research. Keywords: Causal inference, adaptive decision-making, reinforcement learning, game theory, meta learning, interactions with humans.
Lecture notes	Further information will be published on the course website: https://beyond-iid-learning.xyz/
Prerequisites / Notice	BSc in computer science or related field (engineering, physics, mathematics). Passed at least one learning course, such as ``Introduction to Machine Learning" or ``Probabilistic Artificial Intelligence".
263-5210-00L	Probabilistic Artificial Intelligence	8 credits	3V + 2U + 2A	A. Krause
Abstract	This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.
Learning objective	How can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students.
Content	Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning
Prerequisites / Notice	Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.