Teaching

Summer Semester

In summer semester 2024 I am teaching a proseminar and a seminar in we will discuss recent research on interpretability and causality in machine learning.

Proseminar (Bachelor): 2400179 – Interpretierbarkeit und Kausalität im Maschinellen Lernen

Seminar (Master): 2400181 – Interpretability and Causality in Machine Learning

Please sign up to the seminars in the WiWi-Portal

Winter Semester

In winter semester 2024/25 I will teach a course in Geometric Deep Learning:

2400179 – Geometric Deep Learning

This module provides students with both theoretical and practical insights into modern Deep Learning.

In particular, we focus on a novel approach for understanding deep neural networks with mathematical tools from geometry and group theory.

This enables a methodical approach to Deep Learning: starting from first principles of symmetry and invariance, we derive different network architectures for analyzing unstructured sets, grids, graphs, and manifolds.

Topics of the course include: group theory, graph neural networks, convolutional neural networks, applications of geometric deep learning in diverse fields such as geometry processing, molecular dynamics, social networks, game playing (computer Go), processing of text and speech, as well as applications in biomedicine.

Literature: M. M. Bronstein, J. Bruna, T. Cohen, P. Veličković. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
https://arxiv.org/pdf/2104.13478.pdf

Kevin P. Murphy. Machine Learning: A Probabilistic Perspective.
MIT Press, 2012

Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning.
MIT Press, 2017

Parts of the course will be based on the material on https://geometricdeeplearning.com

To subscribe to the lecture please visit the ILIAS course website. The course material will be published on ILIAS throughout the semester as well.