Weekly schedule, lecture notes, R code and extra readings.

Week 1: Introduction and Preliminaries Jan 17 – Jan 21

Week 2: KNN and the Bias-variance trade-Off Jan 24 – Jan 28

Week 3: Linear Models and Model Selection Jan 31 – Feb 4

Week 4: Numerical Optimization Feb 7 – Feb 11

Week 5: Penalized Linear Models Feb 14 – Feb 18

Week 6: Spline Feb 21 – Feb 25

Week 7: Kernel Density Estimation and Local Smoothing Feb 28 – Mar 4

Week 8: Classification Basics Mar 7 – Mar 11

Week 9: Spring Break

Week 10: Support Vector Machine Mar 21 – Mar 25

Week 11: Kernel SVM, RKHS and Kernel Ridge Regression Mar 28 – Apr 1

Week 12: Trees and Random Forests Apr 4 – Apr 8

Week 13: Boosting Apr 11 – Apr 15

Week 14: Clustering Algorithms Apr 18 – Apr 22

Week 15: EM Algorithm and Hidden Markov Model Apr 25 – Apr 29