Recent advances in neural networks have quickly moved the state of the art forward for many applications, not least natural language processing and computer vision. But neural models still have weaknesses, for instance when it comes to reliably reasoning logically or planning. This is were methods from symbolic AI (e.g. rule based and heuristic methods) still dominate. The aim of neuro-symbolic AI is to combine the two approaches to get the best from both worlds. It will also allow us to tackle problems which neither method on its own is particularly good at. In this course, we will focus on two application areas: cognitive modeling and languages for emergent communication, and program synthesis and mathematics. Towards the end of the course, students should have sufficient knowledge to be able to follow recent research papers in these areas.