Lectures

• Jan 13: Overview of Supervised Learning
  Reading: chap 2
• Jan 18, 20: Linear regression model, model selection criteria
  Reading: chap 3.1-3.4.1, 7.1-7.7
• Jan 27: MDL and its application in mining temporal patterns
• Feb 1: Shrinkage method: ridge and lasso
  Reading: chap 3.4.3
• Feb 8: Convex programming problem
• Feb 10, 15, 17 : Splines
  Reading: chap 5.1-5.4
• Feb 22, 24 : RKHS
  Reading: chap 5.8
• Mar 1: Local regression (guest lecture given by Woncheol Jang)
  Reading: Woncheol's nonparametric course website
• Mar 8: Support Vector Machines
  Reading: chap 12
  C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
• Mar 10: History of SVM (guest lecture given by Sayan Mukherjee)
• Mar 22, 24: Linear methods for classification
  Reading: chap 4
• Mar 29: Bootstrap
  Reading: chap 7.11
• Mar 31, Apr 5: Bootsting
  Reading: chap 10
• Apr 7, 12: Tree models
  Reading: chap 9
  Random Forests by Leo Breiman and Adele Cutler
  Multiple Additive Regression Trees (MART) by Jerome H. Friedman
  H. Chipman, E. I. George, and R. E. McCulloch. "Bayesian Treed Models". Machine Learning, 48, 299-320, 2002.