Date	Topic	Notes	Reading
Jan 11	Course overview.
Jan 13	Introduction to AI.
Jan 18	Problem solving as search.
Jan 20	Uninformed search; heuristic search.
Jan 25	A* search; local search.
Jan 27	Adverarial search.
Feb 1	Constraint satisfaction.
Feb 3	Constraint satisfaction (cont).
Feb 8	Knowledge representation.
Feb 10	Propositional logic; reasoning.
Feb 15	First order logic.
Feb 17	Logical inference.
Feb 22	Probability and uncertainty.
Feb 24	Representing uncertain knowledge.
Mar 1	Bayesian networks.
Mar 3	Bayesian networks (cont).
Mar 8	midterm review.
Mar 10	Exact inference algorithms.
Mar 15	SPRING BREAK; NO CLASS.
Mar 17	SPRING BREAK; NO CLASS.
Mar 22	Approximate inference algorithms.
Mar 24	Decision making.
Mar 29	Sequential decision making.
Mar 31	Markov decision process.
Apr 5	Markov decision process (cont).
Apr 7	Reinforcement learning.
Apr 12	Learning as search.
Apr 14	Learning (cont)
Apr 19	Relational learning.
Apr 21	Relational learning II.
Apr 26	Flexible topic.
Apr 28	Final review.