Bernhard Schölkopf: Katalogdaten im Herbstsemester 2021

NameHerr Prof. Dr. Bernhard Schölkopf
LehrgebietEmpirische Inferenz
Professur für Empirische Inferenz
ETH Zürich, CAB H 51
Universitätstrasse 6
8092 Zürich
Telefon+41 44 632 97 34
BeziehungOrdentlicher Professor (affiliiert)

263-5156-00LBeyond iid Learning: Causality, Dynamics, and Interactions Information Belegung eingeschränkt - Details anzeigen
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 KP2SM. Mühlebach, A. Krause, B. Schölkopf
KurzbeschreibungMany 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.
LernzielThe goal of this seminar is to gain experience with machine learning research and foster interdisciplinary thinking.
InhaltThe 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.
SkriptFurther information will be published on the course website:
Voraussetzungen / BesonderesBSc 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".