Teaching Winter 2023/2024
Students will present papers from a list of important recent works in machine learning compiled by the professor. Topics include deep and reinforcement learning, and transformer and GNN architectures. The focus will be in the use of these techniques in simple applications that are crisp and easy to understand. The list of papers will also include those underlying well-known learning systems like as Dalle-E, ChaptGPT, Alphazero, Gato, etc.
The course is devoted to unpack basic questions like: How to make technology work for us? Has it worked for us in the past? Will it work for us in the future? Is there a monolithic “us”? What is actually what we want, collectively and individually? How do we act, collectively and individually? We cannot answer these questions without knowing some history, economics, politics, psychology, and evolutionary biology. In the course, we will make such a tour, and in parts, we will draw on bits of probability, causality, and game theory, to sharpen our critical thinking skills.