Interactive Machine Learning

This course focuses on “interactive machine learning” (iML). We define iML as machine learning in which humans, as a central part, intervene interactively at various points in the learning process. He monitors the results of the machine and provides inputs and corrections to improve the learning process. While traditional ML systems limit human input to providing annotations, iML is concerned with creating interaction design guidelines for ML systems and developing new methods of incorporating human expertise into the ML process. This makes systems more transparent and understandable, which overlaps with the currently emerging research trend of “explainable AI” (XAI). The course consists of several practical hands-on projects and an introduction to various topics related to “interactive machine learning”.

I supervised the course from 2021 to 2026 (SWS 8 in winter terms) and was responsible for the course content and the teaching of the course.

The student projects of previous years are published here:

• 2023
• 2022
• 2021