Lectures 0 and 1:
Administrivia and Introduction
Lecture 2:
Concept Learning and the General-to-Specific Ordering
Lecture 3:
Computational Learning Theory
Lecture 4:
Decision Trees
Lecture 5:
Artificial Neural Networks
Lecture 6:
Evaluating Hypotheses
Lecture 7:
Combining Classifiers: Weighted Majority, Boosting, and Bagging
Lecture 8:
Bayesian Learning
Lecture 9:
Instance-Based Learning
Lecture 10:
Reinforcement Learning