Summary
This semester programme aims to combine optimization and machine learning, in order to develop data-centric methods attaining both performance guarantees and explainability. This includes the design of algorithms incorporating machine-learned predictions of uncertain quality, methods using machine-learned models within algorithms, using machine-learning to model practical problems and using optimization techniques to analyze and improve machine learning algorithms. The programme consists of a bootcamp, a PhD School, two satellite workshops on different aspects of the topic and a seminar++ series. The organizational team comprises diverse international experts in optimization and machine learning, featuring both junior and senior researchers.
