Machine Learning in Montpellier, Theory & Practice – Martin Atzmüller (DFKI / Osnabrück University)

In many applications, modeling complex data is of utmost importance, requiring the use of advanced machine learning models and approaches. However, in many domains users require insight into models and/or their decisions, which is not necessarily provided by the respective models per se. Explainable and interpretable learning approaches can facilitate such insights for making sense of models and decisions. The talk presents examples of such approaches in complex modeling domains, including interpretable as well as explainable deep-learning-based methods, and a neuro-symbolic architecture including domain knowledge for facilitating explainability.