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 13 | Course introduction | |||
| September 14 (x-hour) | Probability theory, part 1 (Andy) | |||
| September 15 | Linear regression | Sec. 1.1 | ||
| September 20 | Non-linear regression; underfitting and overfitting | Sec. 1.2 | ||
| September 21 (x-hour) | Probability theory, part 2 (Sagar) | |||
| September 22 | ML and MAP regression | Sec. 3.1 | ||
| September 27 | Model selection | Sec. 1.3 | hw1 | |
| September 29 | Locally weighted regression | |||
| October 4 | Classification: logistic regression | Sec. 4.3 | ||
| October 6 | Gaussian Discriminant Analysis; Naive Bayes | Sec. 4.2 | ||
| October 11 | kNN; Decision trees | Sec. 2.5, 14.4 | ||
| October 13 | Support Vector Machines | Sec 7.1 | hw2 | hw1 |
| October 18 | Support Vector Machines (part 2) | |||
| October 20 | Midterm exam | |||
| October 25 | Kernels; SMO | |||
| October 27 | k-means | Sec. 9.1 | hw3 | hw2 |
| November 1 | Mixture of Gaussians | 9.2, 9.3 | ||
| November 3 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
| November 8 | Principal Component Analysis | Sec. 12.1 | ||
| November 10 | Multidimensional Scaling | hw3 | ||
| November 15 | Isomap |