Math637 – Spring 2020

Time & Place: MWF 12:20PM – 1:10PM, Room: EWG 204 (Ewing Hall)

Syllabus: pdf version

Textbook: T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.

Available for free at


Date Topic Slides
February 10 Overview pdf
February 12 Linear Regression: old and new pdf
February 17 Linear Regression: old and new (part 2) pdf
February 24 Consistency of Linear Regression pdf
February 26 Subset selection and Coefficients Penalization pdf
March 2 Model selection pdf
March 4 Computing the lasso solution pdf
March 9 Categorical data pdf
March 11 Introduction to statistical decision theory pdf
March 30, April 1 Logistic regression & Linear Discriminant Analysis pdf
April 6 Support vector machines pdf
April 10 Support vector machines and kernels pdf
April 13 Principal component analysis pdf
April 15 Decision trees pdf
April 17 Lab pdf
April 20 Random forest pdf
April 22 Neural networks I pdf
April 24 Neural networks II pdf
April 27 Neural networks Lab pdf
April 29, May 1 The singular value decomposition pdf
May 4, 6 Clustering I pdf
May 8 Clustering II pdf
May 11 Clustering Lab pdf
May 13 The EM algorithm pdf
May 15 Independent component analysis pdf
May 21 Project presentations schedule