CS074/CS174, Spring 2016
Machine Learning and Statistical Data Analysis

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.

DateTopicsReferencesOutDue
March 29Course introduction
March 30 (x-hour)Probability theory, part 1 (Andy)
March 31Linear regressionSec. 1.1
April 5Non-linear regression; underfitting and overfitting Sec. 1.2
April 6 (x-hour)Probability theory, part 2 (Suman)
April 7ML and MAP regressionSec. 3.1
April 12Model selectionSec. 1.3hw1
April 14Locally weighted regression
April 19Classification: logistic regressionSec. 4.3
April 21Gaussian Discriminant Analysis; Naive BayesSec. 4.2
April 26kNN; Decision treesSec. 2.5, 14.4
April 28Support Vector MachinesSec 7.1hw2hw1
May 3Support Vector Machines (part 2)
May 5Midterm exam
May 10Kernels; SMO
May 12k-meansSec. 9.1hw3hw2
May 17Mixture of Gaussians9.2, 9.3
May 19Expectation MaximizationSec. 12.2.2, 12.2.4
May 24Principal Component AnalysisSec. 12.1
May 26Multidimensional Scalinghw3
May 31Isomap