| Week | Dates | Topics & Lecture Notes | Readings |
|---|---|---|---|
| 1 | March 29 | Introduction (ppt, pdf), inductive learning (ppt, pdf) | Mitchell, Ch. 1; Duda, Ch. 1 |
| 2 | April 5 | Decision trees (ppt, pdf) | Mitchell, Ch. 3; Duda, Ch. 8 |
| 3 | April 12 | Rule induction (ppt, pdf) | Mitchell, Ch. 10; Duda, Ch. 8 |
| 4 | April 19 | Instance-based learning (ppt, pdf) | Mitchell, Ch. 8; Duda, Ch. 4 |
| 5 | April 26 | Bayesian learning (ppt, pdf) | Mitchell, Ch. 6; Duda, Ch. 2 & 3 |
| 6 | May 3 | Neural networks (ppt, pdf) | Mitchell, Ch. 4; Duda, Ch. 6 |
| 7 | May 10 | Model ensembles (ppt, pdf) | Duda, Ch. 9 |
| 8 | May 17 | Learning theory (ppt, pdf) | Mitchell, Ch. 7; Duda, Ch. 9 |
| 9 | May 24 | Support vector machines (ppt, pdf) | Duda, Ch. 5 |
| 10 | May 31 | Clustering and dimensionality reduction (ppt, pdf) | Duda, Ch. 10 |
There will be four assignments handed out on weeks 2, 4, 6, and 8; they are due two weeks later. Each assignment is worth 25% of the final grade. Submit online here.