Teaching
Summer term 2021
Winter term 2020/21
Practical course on applying deep learning for image generation.
Introduction to Deep Learning with a focus on image recognition
Summer term 2020
Winter term 2019/20
Introduction to Deep Learning with a focus on image recognition
Bachelor’s and Master’s theses
General requirements
We expect prospective students to have substantial knowledge in machine learning, its mathematical foundations and Python programming. We therefore strongly recommend that students interested in doing their thesis in our lab should take our courses on Machine Learning, Deep Learning and took the Fachpraktikum Data Science. Exceptions are possible if well motivated.
Further recommended lectures are:
- B.Inf.1231: Infrastruktures for Data Science
- M.WIWI-QMW.0002: Advanced Statistical Inference (Likelihood & Bayes)
- B.Inf.1206: Datenbanken
- B.Mat.1300: Numerische lineare Algebra
- B.Mat.2310: Optimierung
Please note, our thesis supervision capacity is limited and we receive more thesis inquiries than we are able supervise. Therefore, we have to select candidates. If you are interested, please write an email with the subject “Master’s thesis” or “Bachelor’s thesis” containing one to three sentences about what you would like to work on and your study record to Alexander Ecker.
We will get back to you within a few days. Otherwise, do not hesitate to remind us :).
