Explore a declarative framework for machine learning in this 45-minute lecture by Martin Grohe from RWTH Aachen University, presented at the Alan Turing Institute. Dive into the concept of logically defined hypotheses, examining both positive and negative learnability results for hypothesis classes defined in first-order and monadic second-order logic. Discover how this theoretical framework could potentially serve as a foundation for declarative approaches to machine learning in logic-oriented fields such as database systems and automated verification. Gain insights into the combination of formal reasoning offered by logic and the power of learning, as part of a workshop aimed at bringing together expertise from various areas to explore the opportunities presented by this intersection.