EML4U: Explainable machine learning for interactive episodic updates of models

Machine Learning (ML) allows complex relationships to be modeled using data. Thus, complex and often grossly simplified mathematical modelling of certain conditions can be avoided. In addition, a new type of functionality opens up: ML models can be adapted to changing requirements and conditions in a data-driven manner. In order to achieve a regular adaptation, data is collected during the use of a model and the model is re-trained taking this information into account. Thus, an episodic update of the ML model takes place.

Funding: German Ministry of Education and Research, BMBF  (06/2020 -- 05/2022)
Contact: Eyke Hüllermeier
Cooperation: Prof. B. Hammer (Bielefeld University) and Semalytix GmbH (Bielefeld)