Interpretable Machine Learning

Prof. Dr. rer. nat. Marius Lindauer
Übungsbetreuung:

Background

The learning objectives include the students acquiring both the theoretical and practical basics of interpretable machine learning (iML). For this purpose, they should internalize the mathematical basics as well as be able to implement, execute and evaluate iML approaches. In a final project, the students will independently apply the learned concepts to a new problem.

Topics

Following topic are covered within the lecture:

Requirements

We strongly recommend that you know the foundations of in order to attend the course. You should have attended at least one other course for ML and DL in the past.

Literature

Dynamics

This course as well as the exercises will be in English only.