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 29 | Course introduction | |||
| March 31 | Linear and non-linear regression | Sec. 1.1 | ||
| April 5 | Probability theory | Sec. 1.2 | ||
| April 6 (x-hour) | Introduction to Matlab | |||
| April 7 | ML and MAP regression; Locally weighted regression | Sec. 3.1 | ||
| April 12 | Project spotlight presentations Model selection | Sec. 1.3 | project proposal | |
| hw1 | ||||
| April 19 | Classification: logistic regression | Sec. 4.3 | ||
| April 21 | Gaussian Discriminant Analysis; Naive Bayes | Sec. 4.2 | ||
| April 26 | kNN; Decision trees | Sec. 2.5, 14.4 | ||
| April 28 | Support Vector Machines | Sec 7.1 | hw2 | hw1 |
| May 3 | Support Vector Machines (part 2) | |||
| May 5 | Kernels; SMO | |||
| May 10 | Project milestone presentations | project milestone | ||
| May 12 | k-means; Mixture of Gaussians | Sec. 9.1, 9.2, 9.3 | hw3 | hw2 |
| May 17 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
| May 24 | Principal Component Analysis | Sec. 12.1 | ||
| May 26 | "How to prepare a bad poster"; Multidimensional Scaling; Isomap | hw3 | ||
| May 31 | Project poster presentations (at 10am, 2nd floor of Hopkins Center for the Arts) | project final write-up |