CPS 271: Spring 1999

Numeric Artificial Intelligence

Preliminary Schedule and Lecture Notes:
Date Topic Reading [Focus]
Thu Jan 14 Introduction & Administrivia (01) (postscript) Ch. 1
Tue Jan 19 Constraint Satisfaction (01) (postscript) Ch. 3 [3.3, 3.5, 3.7]
Thu Jan 21 Satisfiability (03) (postscript) Ch. 6 [6.4, prob. 6.15]
Tue Jan 26 Satisfiability Encodings (04) (postscript) -
Thu Jan 28 Heuristic Search (05) (postscript) Ch. 4 [4.1, 4.2]
Tue Feb 2 Simulated Annealing (06) (postscript) Ch. 4 [4.4], Boyan 3.1, B
Thu Feb 4 Genetic Algorithms (07) (postscript) [20.8]
Tue Feb 9 Least Squares (08) (postscript) -
Thu Feb 11 Information Retrieval (09) (postscript) [23.1]
Tue Feb 16 Gradient Descent: Neural Networks (10) (postscript) Ch. 19 [19.3, 19.4]
Thu Feb 18 Gradient Descent: Graph Layout (11) Ch. 18 [18.3]
Tue Feb 23 Battleship Presentations -
Thu Feb 25 Exam 1: Optimization
Tue Mar 2 Probability (12) (postscript) Ch. 14 [14.2]
Thu Mar 4 Markov Models (13) (postscript) -
Tue Mar 9 Planning Under Uncertainty (14) (postscript) Ch. 17 [17.1]
Thu Mar 11 Solving Markov Decision Processes (15) (postscript) Ch. 17 [17.2, 17.3]
Tue Mar 16 Spring Break
Thu Mar 18
Tue Mar 23 Reinforcement Learning (16) (postscript) Ch. 20 [20.5, 20.6]
Thu Mar 25 Belief Networks (17) (postscript) Ch. 15 [15.1, 15.2]
Tue Mar 30 Learning Belief Networks (18) (postscript) Ch. 19 [19.6]
Thu Apr 1 Hidden Markov Models (19) (postscript) -
Tue Apr 6 More Hidden Markov Models (20) (postscript) -
Thu Apr 8 Partially Observable Markov Decision Processes (21) (postscript) Littman paper
Tue Apr 13 Review -
Thu Apr 15 Exam 2: Probability -
Tue Apr 20 Wrap up: Stage -
Thu Apr 22 Wrap up: TD Gammon -
Tue Apr 27 Final Projects
Thu May 6 Final: 2-5pm

Michael Littman
Mon Mar 1 19:50:39 EST 1999