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 |
|---|---|---|---|---|
| Class cancelled | ||||
| January 7 (x-hour) | Probability theory (part 1) | Sec. 1.2 | ||
| January 8 | Course introduction | |||
| January 13 | Linear regression | Sec. 1.1 | ||
| January 14 (x-hour) | Probability theory (part 2) | |||
| January 15 | Non-linear regression; underfitting and overfitting | |||
| January 20 | ML and MAP regression | Sec. 3.1 | hw1 | |
| January 21 (x-hour) | Model selection | Sec. 1.3 | ||
| January 22 | Locally weighted regression; Project spotlight presentations | project proposal | ||
| January 27 | Classification: logistic regression | Sec. 4.3 | ||
| January 29 | Gaussian Discriminant Analysis; Naive Bayes | Sec. 4.2 | ||
| February 3 | kNN; Decision trees | Sec. 2.5, 14.4 | ||
| February 5 | Support Vector Machines | Sec 7.1 | hw2 | hw1 |
| February 10 | Support Vector Machines (part 2) | |||
| February 12 | Kernels; SMO | |||
| February 17 | Project milestone presentations | project milestone | ||
| February 19 | k-means; Mixture of Gaussians | Sec. 9.1, 9.2, 9.3 | hw3 | hw2 |
| February 24 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
| February 26 | Principal Component Analysis "How to prepare a bad poster" | Sec. 12.1 | ||
| March 3 | Multidimensional Scaling | |||
| March 5 | Isomap; | hw3 | ||
| March 10 | Project final poster presentations (at 10am, Occom Commons in Goldstein Hall) | |||
| March 14 | Final exam (at 11:30am) | |||
| March 15 | final write-up |