Machine Learning, 15:681, Fall 1998

Tentative Course Outline

Professor Tom M. Mitchell School of Computer Science, Carnegie Mellon University

Aug 25, 1998. Overview lecture (optional). (read Chapter 1)

Sept 15. Concept learning, version spaces (ch. 2)

Sept 17. Inductive bias, PAC learning (ch. 2, 6)

Sept 22. PAC learning, VC dimension, Mistake bounds (ch. 6)

Sept 24. Decision trees (ch. 3)

Sept 29. Decision trees, overfitting, Occam's razor (ch. 3)

Oct 1. Neural networks (ch. 4)

Oct 6. Neural networks (ch. 4)

Oct 8. Estimation and confidence intervals (ch. 5)

Oct 13. Boosting, Bagging, and Learning by Committee

Oct 15. Bayesian learning (ch. 6)

Oct 20. Bayesian learning (ch. 6)

Oct 22. Bayesian learning and text (ch. 6)

Oct 27. Midterm exam. open notes, open book.

Oct 29. Incorporating prior knowledge, Bayes nets (ch. 6, 12)

Nov 3. Agents that learn, SOAR, ACT*

Nov 5. Biological learning. Prof. Jay McClelland.

Nov 10. Genetic algorithms, genetic programming (ch. 9)

Nov 12. Learning sets of rules, ILP (ch. 10)

Nov 17. Support Vector Machines

Nov 19. Instance based learning (ch. 8)

Nov 24. Learning Hidden Markov Models

Dec 1. Reinforcement learning (ch. 13)

Dec 3. Reinforcement learning (ch. 13)