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 30 (x-hour) | Probability theory, part 1 (Andy) | |||
March 31 | Linear regression | Sec. 1.1 | ||
April 5 | Non-linear regression; underfitting and overfitting | Sec. 1.2 | ||
April 6 (x-hour) | Probability theory, part 2 (Suman) | |||
April 7 | ML and MAP regression | Sec. 3.1 | ||
April 12 | Model selection | Sec. 1.3 | hw1 | |
April 14 | Locally weighted regression | |||
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 | Midterm exam | |||
May 10 | Kernels; SMO | |||
May 12 | k-means | Sec. 9.1 | hw3 | hw2 |
May 17 | Mixture of Gaussians | 9.2, 9.3 | ||
May 19 | Expectation Maximization | Sec. 12.2.2, 12.2.4 | ||
May 24 | Principal Component Analysis | Sec. 12.1 | ||
May 26 | Multidimensional Scaling | hw3 | ||
May 31 | Isomap |