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Causal Representation Learning
Last Updated: 2026-02-05 15:35:53
Abstract
Deep neural networks have achieved impressive success on prediction tasks in a supervised learning setting, provided sufficient labelled data is available. However, current AI systems lack a versatile understanding of the world around us, as shown in a limited ability to transfer and generalize between tasks.
Objective
The goal of this class is for students to gain experience with advanced research at the intersection of causal inference and deep learning.
Content
The course focuses on challenges and opportunities between deep learning and causal inference, and highlights work that attempts to develop statistical representation learning towards interventional/causal world models. The course will include guest lectures from renowned scientist both from academia as well as top industrial research labs. Deep Representation Learning, Causal Structure Learning, Disentangled Representations, Independent Mechanisms, Causal Inference, World Models and Interactive Learning.
Resources
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- MSC , WBZ
Examination
- Type
- ungraded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar |
Causal Representation Learning
The lecturers will communicate the exact lesson times of ONLINE courses.
|
|
2 h weekly |