Exploring Explainability in Machine Learning

This seminar course delves into the crucial and evolving field of explainability in machine learning (ML). As ML models become increasingly complex and integral to various domains, understanding how these models make decisions is essential. This course will explore different methodologies for interpreting ML models, including rule-based, attribution-based, example-based, prototype-based, hidden semantics-based, and counterfactual-based approaches. Through a combination of paper readings, discussions, and presentations, students will gain a comprehensive understanding of the challenges and advancements in making ML models transparent and interpretable.

More details will be released at https://dcms.cs.uni-saarland.de/explainingml_2425/

Requirements: The student should take a course in machine learning or have sufficient knowledge from other courses; The student should speak English and understand that the seminar will be conducted entirely in English

Places: 8