Date	Topic	Notes	Reading
Jan 14	Course overview.
Jan 16	Introduction to AI.
Jan 21	Problem solving as search.
Jan 23	Uninformed search; heuristic search.
Jan 28	A* search; local search.
Jan 30	Adverarial search.
Feb 4	Constraint satisfaction.
Feb 6	Constraint satisfaction (cont).
Feb 11	Knowledge representation.
Feb 13	Propositional logic; reasoning.
Feb 18	First order logic.
Feb 20	Logical inference.
Feb 25	Probability and uncertainty.
Feb 27	Representing uncertain knowledge.
Mar 3	Bayesian networks.
Mar 5	Bayesian networks (cont).
Mar 10	midterm review.
Mar 12	Exact inference algorithms.
Mar 17	SPRING BREAK; NO CLASS.
Mar 19	SPRING BREAK; NO CLASS.
Mar 24	Approximate inference algorithms.
Mar 26	Decision making.
Mar 31	Sequential decision making.
Apr 2	Markov decision process.
Apr 7	Markov decision process (cont).
Apr 9	Reinforcement learning.
Apr 14	Learning as search.
Apr 16	Learning (cont)
Apr 21	Relational learning.
Apr 23	Relational learning II.
Apr 28	Flexible topic.
Apr 30	Final review.