Winter 2017: EEC 289M Convex Optimization in Electrical Engineering
Spring 2017: ENG 017 Circuits I
Fall 2017: ECE289A Introduction of Reinforcement Learning (T/Th, 10:30~11:50, Wellman Hall Room 1)
This course focuses on the introduction of one important subject of machine learning: reinforcement learning, which is considered the core of artificial intelligence. Topics include fundamentals of reinforcement learning, bandit problems, Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, on-policy vs. off-policy learning, learning vs. planning, approximation methods, eligibility trace, policy gradient methods, and critic-actor methods.
