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ECE-517: Reinforcement Learning in Artificial Intelligence - Course Proposal Course Description Principles and methods for reinforcement learning and sequential stochastic control; Markov decision problems; dynamic programming; temporal difference learning; design considerations for hardware and software. Prereq: Consent of instructor. Credit: 3. Effective: Fall 2007 Textbook R. Sutton and A. Barto, “Reinforcement Learning: An Introduction,” MIT Press, 1998. Course Outline Foundations of machine learning & autonomous cognitive systems Markov decision processes (MDP) Artificial neural networks Dynamic programming Temporal difference (TD) learning Monte Carlo reinforcement learning methods Eligibility traces Hardware & software implementation considerations Supporting Information Rationale: This new graduate course will provide students with indepth knowledge and understanding of diverse theoretical and practical aspects pertaining to stochastic control and applied machine learning. Course has been offered twice (Fall 2005, Fall 2006), with total enrollment of 32 students Course format and location: Standard format, on-campus. Impact on other academic units: None Financial impact: None.