Teaching

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
3D Voxel Model for Representation Learning of Neuronal Morphologies
Supervisor: Laura Hansel
Clustering vs. Continuum
Cluster tendency assessment metrics on high-dimensional data
Supervisor: Laura Hansel
CoreMon: CoreSet Selection for Training Robust Monkey Trackers in Real-World Environments
A coreset selection approach for training robust monkey tracking algorithms for the wild
Supervisor: Sharmita Dey
Design Optimization for Acoustics
Using Neural Networks to predict the frequency responses on beading patterns.
Supervisor: Jan van Delden
Embedding Unbranched Segments of Neuronal Dendrites
Embedding Unbranched Segments of Neuronal Dendrites for Neuron Clustering
Supervisor: Martin Ritzert
Lemur Accelerometer
Automatic Detection of Lemur Behaviors from Accelerometer Data
Supervisor: Dr Kaja Wierucka, Richard Vogg
Lemur Vocalization
Automatic Detection, Segmentation and Classification of Lemur Vocalizations
Supervisor: Dr Kaja Wierucka, Richard Vogg
Self-Supervised Pretraining for Training Robust Monkey Detection and Tracking Models
Leveraging self-supervised pretraining to improve the robustness of monkey detection and tracking models in diverse environmental conditions.
Supervisor: Sharmita Dey
Solving Citation Networks with Large Language Models
By focussing on the abstract, LLMs should be able to effectively solve Cora and other citation datasets
Supervisor: Martin Ritzert
Treewidth-Based Positional Encodings
Generalizing a positional encoding for trees to general graphs using the notion of treewidth
Supervisor: Martin Ritzert
Neural Data Science Group
Institute of Computer Science
University of Goettingen