Teaching

Summer term 2023

Practical course on applying deep learning for image generation.

Alexander Ecker and Timo Lüddecke

Introduction to Machine Learning

Alexander Ecker

Winter term 2022/2023

Seminar where recent deep learning papers are presented and discussed.

Alexander Ecker, Laura Hansel, Richard Vogg, Polina Turishcheva and Timo Lüddecke

Summer term 2022

Seminar where recent computational neuroscience papers are presented and discussed.

Alexander Ecker, Laura Pede, Michaela Vystrčilová, Suhas Shrinivasan

Practical course on applying deep learning for image generation.

Alexander Ecker and Timo Lüddecke

Introduction to Machine Learning

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:

M.Inf.2201: Probabilistic Machine Learning (by Fabian Sinz)
B.Inf.1231: Infrastructures for Data Science (by Philipp Wieder)
M.WIWI-QMW.0002: Advanced Statistical Inference (by Elisabeth Bergherr)

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 the supervisor stated below.

We will get back to you within a few days. Otherwise, do not hesitate to remind us :).

Thesis offers

3D Voxel Model for Representation Learning of Neuronal Morphologies

Supervisor: Laura Hansel

Cluster tendency assessment metrics on high-dimensional data

Supervisor: Laura Hansel

A coreset selection approach for training robust monkey tracking algorithms for the wild

Supervisor: Sharmita Dey

Using Neural Networks to predict the frequency responses on beading patterns.

Supervisor: Jan van Delden

Embedding Unbranched Segments of Neuronal Dendrites for Neuron Clustering

Supervisor: Martin Ritzert

Automatic Detection of Lemur Behaviors from Accelerometer Data

Supervisor: Dr Kaja Wierucka, Richard Vogg

Automatic Detection, Segmentation and Classification of Lemur Vocalizations

Supervisor: Dr Kaja Wierucka, Richard Vogg

Leveraging self-supervised pretraining to improve the robustness of monkey detection and tracking models in diverse environmental conditions.

Supervisor: Sharmita Dey

By focussing on the abstract, LLMs should be able to effectively solve Cora and other citation datasets

Supervisor: Martin Ritzert

Generalizing a positional encoding for trees to general graphs using the notion of treewidth

Supervisor: Martin Ritzert