Teaching

Teaching

Summer term 2021

Introduction to Machine Learning

Alexander Ecker

Winter term 2020/21

Practical course on applying deep learning for image generation.

Alexander Ecker and Timo Lüddecke

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.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 :).

Thesis offers

Deep Learning for Particle Tracking
Master’s thesis in collaboration with Bodenschatz Turbulence Group (MPI for Dynamics and Self Organization, Göttingen)
Supervisor: Alexander Ecker and Jan Molacek
Design and implementation of a cubist mirror
Build a “cubist mirror” – an interactive setup that transforms a live video stream into artistic styles
Supervisor: Alexander Ecker
Domain-specific self-supervised learning
Use self-supervised learning as pre-training in specific domains
Supervisor: Timo Lüddecke
Object-centric representation learning
Building a unified framework for object-centric representation learning models.
Supervisor: Marissa Weis
Self-Supervised Learning for HI-RES Tree Cover and Tree Class Segmentation
Learning generic features for remote sensing data, specifically forests
Supervisor: Timo Lüddecke and Nils Nölke
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