Michael Mühlebach: Catalogue data in Autumn Semester 2021 |
Name | Dr. Michael Mühlebach |
Address | Max Planck Institut für Intelligente Systeme Max Planck Ring 4 72076 Tübingen GERMANY |
Telephone | 004970716011904 |
michael.muehlebach@inf.ethz.ch | |
Department | Computer Science |
Relationship | Lecturer |
Number | Title | ECTS | Hours | Lecturers | |
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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". |