Bachelor and Master thesis projects can be found here.
Important: registration for courses is exclusive via CAMPO and StudOn.
Biomedical Image Analysis Project [BIMAP]
We offer projects in a structured way only in the summer semester (5 and 10 ECTS). Please apply in time for a spot, as this is highly frequented (10-12 spots and ~ 100 applicants per seminar). After acceptance, you will receive the list of available projects and you can select your priorities.
Tracking Olympiad [TRACO]
This 5 ECTS seminar is only offered in the summer semester. In this seminar, you will learn to detect and track Hexbugs, voluntarily moving micro robots. More information can be found on the seminar’s micro website.
Data Science Survival Skills [DSSS]
| VL+UE (2 SWS VL, 2 SWS Exercises) | 5 ECTS | WS 2023/2024 tbd |
In DSSS, we teach survival skills, meaning knowledge that is crucial for a career in data science and to work very efficiently. We will cover among others the following topics intensively:
- File formats for text, images, structured and arbitrary data
- Multithreading and multiprocessing
- Just-in-time compilation using numba
- Multithreading and multiprocessing
- Prototyping graphical user interfaces
- No code environments
Fantastic datasets and where to find them [FANDAT]
| SEM (2 SWS) | 5 ECTS | WS 2023/2024 tbd. |
In this seminar, we investigate where to get (biomedical) datasets, how to look for them, and how to evaluate them. What turns a dataset good or bad? How is it used in the community? And what impact do they have in machine and deep learning? Aim of the seminar is to create your own open educational resource (OER) about a given dataset that also will be peer-reviewed by your fellow students.
Cognitive Neuroscience for AI Developers [CNAID]
| VL (4 SWS) | 5 ECTS | SS 2023: Wed 10-12, Thu 14-16 |
CNAID is a lecture designed by Patrick Krauß, Andreas Maier and Andreas M Kist. We highlight the basics of Cognitive Neuroscience and discuss the biological implementations of neural networks. We further connect biology and artificial intelligence by giving specific examples of how both are linked together, and how aspects of AI are inspired by biology.