| Date | Topic | Reading Assignment | Presenter
|
|---|
| T 9 JAN | Introduction |
|
Bahler |
| H 11 JAN | Overview |
Ch. 11 ("Learning") in
David Poole, Alan Mackworth, and Randy Goebel,
Computational Intelligence: A Logical Approach,
New York: Oxford, 1998.
|
Bahler |
| T 16 JAN | No Class |
|
|
| H 18 JAN | Evaluating Learning Systems |
Ch. 2 ("How to Estimate the True Performance of a Learning System") in
S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn,
San Francisco: Morgan Kaufmann, 1991.
|
Bicici |
| T 23 JAN | No Class |
|
|
| H 25 JAN | Prescient Prehistory |
Arthur Samuel,
"Some Studies in Machine Learning Using the Game of Checkers,"
IBM Journal of Research and Development 3(3), July, 1959.
Reprinted in
E. A. Feigenbaum and J. Feldman (Eds.),
Computers and Thought,
New York: McGraw Hill, 1963.
|
Bailey |
| T 30 JAN | Reinforcement Learning I |
Kaelbling, L.P., M.L. Littman, and A.W. Moore,
"Reinforcement Learning: A Survey,"
Jour. of Artificial Intelligence Research 4,
1996, 237-285.
| Yolum |
| H 1 FEB | Reinforcement Learning II | " | Mallya |
| T 6 FEB | Reinforcement Learning III |
Sutton, R.S.,
"Learning to Predict by the Methods of Temporal Differences,"
Machine Learning 3, 1988, 9-44.
| Chatterjee |
| H 8 FEB | " | " | " |
| T 13 FEB | Reinforcement Learning IV |
Tesauro, G.,
"Practical Issues in Temporal Difference Learning,"
Machine Learning 8, 1992, 257-277.
| Wilson |
| H 15 FEB | Decision Tree Induction I |
J.R. Quinlan,
"Induction of Decision Trees"
Machine Learning 1:81-106,
1986.
|
Poon |
| T 20 FEB | Decision Tree Induction II |
Mingers, J.,
"An Empirical Comparison of Pruning Methods for Decision Tree
Induction,"
Machine Learning 4(2), 1989, 227-243.
|
Wei |
| H 22 FEB | Decision Tree Induction III |
Bahler, D. and D.W. Bristol,
"The Induction of Rules for Predicting Chemical Carcinogenesis in
Rodents," in
L. Hunter, J. Shavlik, and D. Searls, eds.
Intelligent Systems for Molecular Biology,
Menlo Park, CA: AAAI/MIT Press, 1993, 29-37.
|
Bicici |
| T 27 FEB | Inductive Logic Programming I |
Mitchell, T.M.,
"Generalization as Search,"
Artificial Intelligence 18, 1982, 203-226.
| Yolum |
| H 1 MAR | Inductive Logic Programming II |
Srinivasan, A., S.H. Muggleton, M.J.E. Sternberg, and R.D. King,
"Theories for Mutagenicity: A Study in First-Order and Feature-Based
Induction,"
Artificial Intelligence 85(1/2), 1996, 277-299.
| Mallya |
| T 6 MAR | Inductive Logic Programming III |
Lavrac, N. and S. Dzeroski,
Inductive Logic Programming: Techniques and
Applications, New York: Ellis Horwood, 1994
(excerpts).
| Chatterjee |
| H 8 MAR | " | " | Wei |
| F 9 MAR |
| Drop Date |
|
| T 13 MAR | SPRING BREAK |
|
|
| H 15 MAR | SPRING BREAK |
|
|
| T 20 MAR |
| MID-PROJECT PROGRESS REPORTS (10 min.) |
|
| T 22 MAR | Introduction to Bayesian Models and Learning |
Jensen, F.V., An Introduction to Bayesian Networks,
New York: Springer, 1996 (excerpts). | Bahler |
| T 27 MAR | Bayesian Learning |
Heckerman, D.,
"A Tutorial on Learning With Bayesian Networks,"
Tech. Rept. MSR-TR-95-06, Redmond WA: Microsoft
Research, 1995 (Revised 1996). | Wilson |
| H 29 MAR | " | " | Bicici |
| T 3 APR | " | " | " |
| H 5 APR | MDL Bayesian Learning |
Lam, W. and F. Bacchus,
"Learning Bayesian Belief Networks: An Approach Based on the MDL
Principle,"
Computational Intelligence 10(3), 1994, 269-293.
| Bailey |
| T 10 APR | Neural Models |
Hertz, J., A. Krogh, and R.G. Palmer,
Introduction to the Theory of Neural Computation,
Reading MA: Addison-Wesley, 1991 (excerpt).
| Poon |
| H 12 APR | NO CLASS |
|
|
| T 17 APR | Neural Applications |
Bahler, D. and B. Stone,
"Neural Models and Extracted Rules for Knowledge Discovery in
Predictive Toxicology," (to appear).
| Yolum |
| H 19 APR | Mixed Applications |
Dennis Bahler, C. Wellington, B. Stone, and D. Bristol,
"Symbolic, Neural, and Bayesian Machine Learning
Models for Predicting Carcinogenicity of Chemical Compounds,"
J. Chemical Information and Computer Sciences 40 (4),
July/August 2000, 906-914.
| Mallya |
| T 24 APR | Unsupervised Learning via Clustering |
Fasulo, D.,
"An Analysis of Recent Work on Clustering Algorithms,"
Tech. Rept. 01-03-02, Dept. of Computer Science and Engineering,
Univ. of Washington, 1999.
| Chatterjee |
| H 26 APR | Relevance Learning and Feature Selection |
Blum, A. L. and P. Langley,
"Selection of Relevant Features and Examples in Machine Learning,"
Artificial Intelligence 97(1-2), 1997, 215-234.
| Wilson |
| T 1 MAY |
| PROJECT PRESENTATIONS (15 min.) |
|
| H 3 MAY |
| PROJECT PRESENTATIONS (15 min.),Exam Out |
|
| F 4 MAY | Written Project Reports Due 1700 |
|
|
| T 15 MAY | Final Exam Due 1600 |
|
|