The course is about prediction and control using reinforcement learning, including aspects of deep reinforcement learning, i.e., the application of neural networks-based functional approximation to reinforcement learning problems. The course covers theory and applications related to the following topics: Markov decision processes. Value function approximation. Policy gradient methods, Actor-critic algorithms. Integration of Learning and Planning. Exploration vs exploitation trade-offs. The course uses active learning techniques to guarantee better engagement from the students. Also, experts from the industry are invited to talk about the practical applications in this domain. This course includes assignments and a practical term project.