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 |
|---|---|---|---|---|
| March 30 | Course introduction | |||
| March 31 (x-hour) | Introduction to Matlab, part 1 (Qingyuan) | |||
| April 1 | Linear and non-linear regression | Sec. 1.1 | ||
| April 6 | Probability theory | Sec. 1.2 | ||
| April 7 (x-hour) | Introduction to Matlab, part 2 (Qingyuan) | |||
| April 8 | ML and MAP regression; Locally weighted regression | Sec. 3.1 | hw1 | |
| April 13 | Project spotlight presentations Model selection | Sec. 1.3 | project proposal | |
| April 15 | Classification: logistic regression | Sec. 4.3 | ||
| April 22 | Gaussian Discriminant Analysis; Naive Bayes | Sec. 4.2 | hw2 | hw1 |
| April 27 | kNN; Decision trees | Sec. 2.5, 14.4 | ||
| April 29 | Support Vector Machines | Sec 7.1 | ||
| May 4 | Support Vector Machines (part 2) | |||
| May 6 | Kernels; SMO | hw3 | hw2 | |
| May 11 | Project milestone presentations | |||
| May 12 (x-hour) | Project milestone presentations | project milestone | ||
| May 13 | k-means; Mixture of Gaussians | Sec. 9.1, 9.2, 9.3 | ||
| May 18 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
| May 20 | Principal Component Analysis | Sec. 12.1 | hw4 | hw3 |
| May 25 | "How to prepare a bad poster"; Multidimensional Scaling; Isomap | |||
| June 1 | Project poster presentations (at 10am, 2nd floor of Hopkins Center for the Arts) | project final write-up | ||
| June 2 | hw4 |