COS402: Artificial Intelligence
Fall 2006

15.4

Day	Topic	Reading	Optional Reading
9/14/06	What is AI? (PDF of slides)	RN 1-2	Computing Machinery and Intelligence by A. Turing
9/19/06	Problem Solving: Uninformed Search	RN 3
9/21/06	A* Search and Heuristic Functions	RN 4.1, 4.2	Finding Optimal Solutions to Rubik's Cube Using Pattern Databases by R. Korf
9/26/06	Local Search: Searching in Games	RN 4.3, 6
9/28/06	Propositional Logic	RN 7 (skim 7.6-7.8)
10/3/06	Propositional Logic (II) and First Order Logic	RN 8
10/5/06	Uncertainty and Probability	RN 13
10/10/06	Bayesian Networks Semantics	RN 14.1-14.3
10/12/06	The Bayes Ball algorithm	Jordan Ch 2.1
10/17/06	The Elimination Algorithm	RN 14.4
10/19/06	Markov chain Monte Carlo (MCMC)	RN 14.5
10/24/06	MCMC (cont); Hidden Markov models	RN 15.1-15.3
10/26/06	Hidden Markov models (cont) and the Kalman filter
10/31/06	FALL RECESS
11/2/06	FALL RECESS
11/7/06	Markov Decision Processes I	RN 16.1-16.3
11/9/06	Markov Decision Processes II	RN 17.1-17.3
11/14/06	Markov Decision Processes III
11/16/06	Reinforcement Learning I	RN 21.1-21.3
11/21/06	Reinforcement Learning II	RN 21.4-21.6	Learning to Play Chess Using Temporal Differences
11/23/06	THANKSGIVING
11/28/06	Machine Learning and Naive Bayes	RN 20.1-20.2
11/30/06	Naive Bayes Continued
12/5/06	Neural Networks and the Perceptron	RN 20.5
12/7/06	Support Vector Machines and Kernel Methods	RN 20.6	Support vector machine tutorial
12/12/06	Boosting	Boosting overview
12/14/06	The Future of AI