This page will be updated frequently with current and upcoming topics. Chapter references, when available, are to the recommended course textbook, Pattern Recognition and Machine Learning.
| Date | Topics | References | Out | Due |
|---|---|---|---|---|
| September 16 | Course introduction | |||
| September 17 (x-hour) | Introduction to Matlab | |||
| September 18 | Linear and non-linear regression | Sec. 1.1 | ||
| September 23 | Probability theory | Sec. 1.2 | ||
| September 25 | ML and MAP regression | Sec. 3.1 | ||
| September 30 | Model selection | Sec. 1.3 | hw1 | |
| October 2 | Locally weighted regression; Project spotlight presentations | project proposal | ||
| October 7 | Classification: logistic regression | Sec. 4.3 | ||
| October 9 | Gaussian Discriminant Analysis; Naive Bayes | Sec. 4.2 | ||
| October 14 | kNN; Decision trees | Sec. 2.5, 14.4 | ||
| October 16 | Support Vector Machines | Sec 7.1 | hw2 | hw1 |
| October 21 | Support Vector Machines (part 2) | |||
| October 23 | Kernels; SMO | |||
| October 28 | Project milestone presentations | project milestone | ||
| October 30 | k-means; Mixture of Gaussians | Sec. 9.1, 9.2, 9.3 | hw3 | hw2 |
| November 4 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
| November 6 | Principal Component Analysis "How to prepare a bad poster" | Sec. 12.1 | ||
| November 11 | Multidimensional Scaling | |||
| November 13 | Isomap; | hw3 | ||
| November 18 | Project final poster presentations (at 10am, Occom Commons in Goldstein Hall) | |||
| November 21 | final write-up | |||
| November 24 | final exam |