# Teaching

### Winter term 2020/21

Introduction to Deep Learning with a focus on image recognition

Alexander Ecker

### Summer term 2020

Introduction to Machine Learning

Alexander Ecker

### Winter term 2019/20

Introduction to Deep Learning with a focus on image recognition

Alexander Ecker

# 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.1206: Datenbanken
- B.Mat.1300: Numerische lineare Algebra
- B.Inf.1231: Infrastrukturen für Data Science
- B.Mat.2310: Optimierung
- M.WIWI-QMW.0002: Advanced Statistical Inference (Likelihood & Bayes)

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 thesis” or “bachelor 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 :).

## Thesis offers

Use self-supervised learning as pre-training in specific domains

Supervisor: Timo Lüddecke

Building a unified framework for object-centric representation learning models.

Supervisor: Marissa Weis

Learn a feature extractor for video.

Supervisor: Timo Lüddecke

Apply techniques for novel view synthesis with contrastive self-supervised learning

Supervisor: Timo Lüddecke