Prof. Dr. Felix Dietrich
Address:
Technische Universität München
TUM School of CIT
Department of Computer Science
Boltzmannstrasse 3
85748 Garching
Germany
Tel: +49-89-289-18638
Fax: +49-89-289-18607
Office: MI 02.05.041
Email: felix.dietrich (at) tum.de
Office Hours: by arrangement (email)
Student projects
Use the project application page to apply. It will send me an email with all the required information already filled.
There are several topics for Master's theses (they can be scaled down for Bachelor's theses as well):
- Look at the slides and the latter half of the video on machine learning topics
- Generally, topics relating physics / dynamical systems / algorithms and machine learning (e.g. see our papers on Koopman operators , fast training of neural networks, and learning algorithms)
- If you have something else in mind, let me know!
- Always use the project application page.
Currently Teaching
Winter term 2023/24
| Title | Type | Lecturer (assistant) |
|---|---|---|
| Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) | PR |
|
Summer term 2024
| Title | Type | Lecturer (assistant) |
|---|---|---|
| Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267) | PR |
|
Previous Teaching @TUM
Winter 2022/23
Summer 2022
- Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267)
- Seminar High Dimensional Methods in Scientific Computing (IN2107,IN0014,IN2183)
Winter 2021/22
- Computational Aspects of Machine Learning (IN2107, IN0014, IN2183)
- Master-Praktikum - Machine Learning in Crowd Modeling & Simulation (IN2106, IN4267)
Summer 2021
- Bachelor & Master Seminar: High Dimensional Methods in Scientific Computing
- Machine Learning in Crowd Modeling and Simulation
Winter 2020/21
- Hauptseminar Computational Aspects of Machine Learning
- Machine Learning in Crowd Modeling and Simulation
Summer 2020
- Algorithms for Scientific Computing
- Bachelor & Master Seminar: High Dimensional Methods in Scientific Computing
- Machine Learning in Crowd Modeling and Simulation
Winter 2019/20
Winter 2015/16
Summer 2015