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 31 | Course introduction; linear regression | Sec. 1.1 | ||
| April 2 | Non-linear regression; probability theory | Sec. 1.2 | ||
| April 14 | ML and MAP regression; locally weighted regression; model selection | |||
| April 16 | Logistic regression; GDA | Sec. 4.2, 4.3 | hw1 | |
| April 21 | Project spotlight presentations Naive bayes; KNN | project proposal | ||
| April 28 | Decision trees | Sec. 14.4 | ||
| April 30 | Support Vector Machines | Sec. 7.1 | hw2 | hw1 |
| May 5 | Support Vector Machines (part 2); k-means | Sec. 9.1 | ||
| May 7 | Mixture of Gaussians; Expectation Maximization | Sec. 9.2, 9.3 | ||
| May 12 | EM for Mixture of Gaussians, Mixture of Naive Bayes and Factor Analysis | Sec. 12.2.2, 12.2.4 | project milestone | |
| May 14 | Principal Component Analysis | Sec. 12.1 | hw3 | hw2 |
| May 19 | Multidimensional Scaling; Isomap | |||
| May 21 | Hidden Markov Models | Tutorial by L. Rabiner | ||
| May 26 | "How to prepare a bad poster"; Linear Dynamical Systems | |||
| May 28 | The bias/variance tradeoff; "How to make a learning algorithm work" | hw3 | ||
| June 2 | Project poster presentations (at 10am in Alumni Hall, Hopkins Center for the Arts) | project final write-up |