Bachelor and master thesis will be offered from various research areas. Specific topics will be defined by the chair or in collaboration with the student.

To write a thesis at the chair of Explainable Machine Learning, the following qualifications need to be fulfilled:

  • successful exam in a module with lecture and tutorial in Deep Learning or Mathematics for Machine Learning (MSc Thesis), or Machine Learning or Introduction to AI (BSc Thesis)
  • successful participation at one of the chair offered seminars or projects

Open thesis

Please refer to VC [Link] for further details.

Current thesis

  • "Unveiling CNN Layer Contributions: Application of Feature Visualization in Medical Image Classification Tasks" - Jonida Mukaj supervised by Ines Rieger
  • "Generative Data Augmentation in the Embedding Space of Vision Foundation Models to Address Long-Tailed Learning and Privacy Constraints" - David Elias Tafler supervised by Francesco Di Salvo

Finished thesis