| 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 | ||