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 |
|---|---|---|---|---|
| January 8 | Course introduction | |||
| January 9 (x-hour) | Introduction to Matlab | |||
| January 10 | Linear and non-linear regression | Sec. 1.1 | ||
| January 15 | Probability theory | Sec. 1.2 | ||
| January 16 (x-hour) | TBD | |||
| January 17 | ML and MAP regression | Sec. 3.1 | hw1 | |
| January 22 | Model selection | Sec. 1.3 | ||
| January 23 (x-hour) | TBD | |||
| January 24 | Locally weighted regression; Project spotlight presentations | project proposal | ||
| January 29 | Classification: logistic regression | Sec. 4.3 | ||
| January 30 (x-hour) | TBD | |||
| January 31 | Gaussian Discriminant Analysis; Naive Bayes | Sec. 4.2 | hw2 | hw1 |
| February 5 | kNN; Decision trees | Sec. 2.5, 14.4 | ||
| February 6 (x-hour) | TBD | |||
| February 7 | Support Vector Machines | Sec 7.1 | ||
| February 12 | Support Vector Machines (part 2) | |||
| February 13 (x-hour) | TBD | |||
| February 14 | Kernels; SMO | hw3 | hw2 | |
| February 19 | Project milestone presentations | project milestone | ||
| February 21 | k-means; Mixture of Gaussians | Sec. 9.1, 9.2, 9.3 | ||
| February 26 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
| February 28 | Principal Component Analysis "How to prepare a bad poster" | Sec. 12.1 | hw3 | |
| March 5 | Multidimensional Scaling; Isomap; | |||
| March 7 | Project poster presentations (at 10am, 2nd floor of Hopkins Center for the Arts) | |||
| March 8 | project final write-up |