In recent years, modern machine learning systems have achieved unprecedented success in learning from data with minimal human guidance. In parallel to the advancements in AI, Cognitive Science has been very successful at applying a variety of computational models to human learning. Still, computational and cognitive learners are often ‘black-boxes’ lacking interpretation and explanation. How can we reason about, understand, and guide computational learning processes?

In this course, we introduce an approach for reasoning about learning that takes inspiration from Dynamic Epistemic Logic. Our lectures will feature both classical problems in learning and recent results about dynamic logics of learning. We also provide extra exercises and additional reading for those interested in a deeper understanding (See the Resources tab).

Our target audience for this course is interdisciplinary, including students with backgrounds in mathematical logic, theoretical computer science, and formal philosophy, but also cognitive and social science.