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 recommend that students interested in doing their thesis in our lab should take our courses on Machine Learning and Deep Learning.

Below, we provide a list of topics for theses. If you are interested in one of these topics and fullfill the mentioned requirements, please get in touch with the supervisor by mail.

Thesis offers

Domain-specific self-supervised learning
Use self-supervised learning as pre-training in specific domains
Supervisor: Timo Lüddecke
Self-supervised learning on video
Learn a feature extractor for video.
Supervisor: Timo Lüddecke
Self-supervised learning using view synthesis
Apply techniques for novel view synthesis with contrastive self-supervised learning
Supervisor: Timo Lüddecke
Neural Data Science Group
Institute of Computer Science
University of Goettingen