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Beyond iid Learning: Causality, Dynamics, and Interactions
Last Updated: 2026-02-05 15:48:25
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.
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.
Resources
Lecture Notes
Further information will be published on the course website:https://beyond-iid-learning.xyz/
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- MSC , WBZ
Examination
- Type
- ungraded semester performance
Registration & Places
- Max Places
- 60
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar |
Beyond iid Learning: Causality, Dynamics, and Interactions
The lecturers will communicate the exact lesson times of ONLINE courses.
|
|
2 h weekly |