|
Topic
|
Material
|
| Week 1 | Course Introduction, Introduction to Machine Learning, Fundamentals of Concept Learning |
| Week 2 | Fundamentals of Concept Learning (Revised), Concept Learning Exercises, Decision Tree Learning |
| Week 3 | Rule Learning, Note on Support |
| Week 4 | Machine Learning for Numeric Prediction, Revised Notes |
| Week 5 | Instance Based Learning, Genetic Algorithms |
| Week 6 | MidTerm Review, Bias |
| Week 7 | Evaluating hypotheses, ML_part_V.pdf |
| Week 8 | Bayesian Learning |
| Week 9 | Guest Lecture: Reinforcement Learning |
| Week 10 | Learning Theory, Supplementary, Supplementary (corrected) |
| Week 11 | Ensembles, etc., NFL |
| Week 12 | Unsupervised Learning |
| Week 13 | Learning and Logic, Relational learning on text |
| Week 14 | Final exam 2003, Some topics |