COS402: Artificial Intelligence
Fall 2006


Information
Syllabus
Assignments

Syllabus

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