Michael Mühlebach: Catalogue data in Autumn Semester 2021

Name Dr. Michael Mühlebach
Max Planck Institut
für Intelligente Systeme
Max Planck Ring 4
72076 Tübingen
DepartmentComputer Science

263-5156-00LBeyond iid Learning: Causality, Dynamics, and Interactions Information Restricted registration - show details
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 credits2SM. Mühlebach, A. Krause, B. Schölkopf
AbstractMany 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.
ObjectiveThe goal of this seminar is to gain experience with machine learning research and foster interdisciplinary thinking.
ContentThe 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 notesFurther information will be published on the course website: https://beyond-iid-learning.xyz/
Prerequisites / NoticeBSc 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".