Seminar: Explainable Machine Learning (WS 2021/2022)


Current publications on machine learning / computer vision are covered in this seminar. In particular, topics from the area of explainable machine learning (in particular vision and language based deep learning, attention models, transformers) are in focus.

This is a Master's level course. Since these topics are very complex, prior participation in at least one of the following lectures is required:

  • Deep Learning
  • Probabilistic Machine Learning
  • Statistical Machine Learning


The schedule of the seminar is as follows:

  • October 20th, 2-6pm
  • October 27th, 2-6pm
  • November 3rd, 2-6pm
  • November 10th, 2-6pm
  • November 17th, 2-6pm
  • November 24th, 2-6pm
  • December 1st, 2-6pm

All seminars will take place on zoom. All the accepted participants will receive the zoom link on their email that they used in ILIAS.

The course awards 3 LP Credits.


A successful participation in the seminar includes:

  • Active participation in the entire event: We have 70% attendance policy for this seminar. You need to attend at least 5 of the 7 sessions.
  • Short presentation on October 27th or November 3rd (10 minutes talk, 5 min questions)
  • Presentation on November 10th, November 17th, November 24th or December 1st (20 minutes talk, 10 minutes questions) on a selected topic

Topics to be covered

Interpretability in psychology and cognitive sciences:

Machine Attention as Explanations in Computer Vision:

Communication-based learning for natural image data:

Generating Natural Language Explanations in Computer Vision:

Compositional Learning:


The registration opens on October 1st via ILIAS.