Aug 24	Introduction and course overview	Lecture 1
Aug 26	Introduction to AI applications	Lecture 2
Aug 31	Search in AI	Lecture 3
Sept 2	Uninformed and heuristic search	Lecture 4
Sept 7	heuristic search and simulated annealing	Lecture 5
Sept 9	Local beam search and genetic algorithm	Lecture 6
Sept 14	Genetic algorithm and Constraint Satisfaction Problem (CSP)	Lecture 7
Sept 16	CSP, Backtracking search, Heuristics, Constraint propagation	Lecture 8
Sept 21	Constraint propagation, Local search with min-conflict heuristic	Lecture 9
Sept 23	Phase change, Cutset conditioning, Tree decomposition,	Lecture 10
Sept 28	Adversarial Search, Zero-sum games, MinMax, Alpha-Beta algorithm	Lecture 11
Sept 30	Evaluation functions and adaptive deepening	Lecture 12
Oct 5	Adversarial Search and Uncertainty	Lecture 13
Oct 7	Probability and Bayes' theorem	Lecture 14
Oct 14	Conditional independence	Lecture 15
Oct 19	Bayesian networks, Markov blanket	Lecture 16
Oct 21	Midterm review	Lecture 17
Oct 28	Bayesian networks, Explaining away, Variable elimination	Lecture 18
Nov 2	Variable elimination, Monte Carlo methods	Lecture 19
Nov 4	Rejection sampling and importance sampling	Lecture 20
Nov 9	Hidden Markov models	Lecture 21
Nov 11	Introductoin to learning, decision trees	Lecture 22
Nov 16	Decision trees, Entropy, Information gain	Lecture 23
Nov 18	Mutual information, Overfitting and cross-validation	Lecture 24
Nov 23	Tree pruning, Linear classification, Nearest neighbor classifier	Lecture 25
Nov 30	Nearest neighbor classifier, Peceptron	Lecture 26
Dec 2	Naive Bayes, Structure learning for Bayesian networks	Lecture 27
Dec 7	Final review	Lecture 28