Teaching

Seminar: Explainable Machine Learning

Content

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

Organisation

The schedule of the seminar is as follows:

  • November 4th, 2-6pm (Slides)
  • November 11th, 2-6pm
  • November 18th, 2-6pm
  • November 25th, 2-6pm
  • December 2, 2-6pm
  • December 9, 2-6pm
  • December 16, 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.

Requirements

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 November 11th or 18th (10 minutes talk, 5 min questions)
  • Presentation on November 25th, December 6, 9 or 16th (20 minutes talk, 10 minutes questions) on a selected topic


Topics to be covered

Interpretability in psychology and cognitive sciences:

Machine Attention in Natural Language Processing and Computer Vision:

Human attention:

Textual and visual explanations for natural image data:                

Textual and visual explanations for medical image data:

Compositional Learning:

Combining classical decision trees and deep learning:

Registration

The registration opens on October 5th via ILIAS.