The elective course in “Reinforcement Learning” covers the theory and application of a machine learning paradigm in which an agent learns to make decisions in an environment to maximize a reward signal. Topics include Markov Decision Processes, Bellman equations, value and policy iteration, Monte Carlo methods, temporal difference learning, Q-learning, policy gradients, and deep reinforcement learning. Applications in robotics, game playing, and recommendation systems may also be discussed. Hands-on projects and assignments are used to reinforce the concepts learned.