This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approac
This course focuses on dynamic models of random phenomena, and in particular, the most popular classes of such models: Markov chains and Markov decision processes. We will also study applications in q
The goal of this course is to instruct the student how fundamental scientific knowledge, acquired through the study of fundamental disciplines, including biochemistry, genetics, pharmacology, physiolo
Animals must learn from past experiences, to adapt their behavior to an ever-changing environment. Students will learn about the neuronal circuit mechanisms of reward-based learning, and of aversively
Surprise, Reward, and Curiosity are drives of human, animal, and robot behavior. The class links theories of reinforcement learning with human behavior beyond standard notions of reward.
