Machine Learning (ML) and Control Theory (CT) are two closely interconnected disciplines with highly attractive research problems. This project aims to encourage the application of CT tools in ML and vice versa, and to explore the great application potential of combining these two rapidly evolving fields. Particular emphasis will be placed on the use of control-theoretic tools for the analysis of deep neural networks, on developing algorithms for efficiently solving parameter-dependent control tasks, and on the development of effective and reliable data-driven control methods.