MATH 680: Computation Intensive Statistics

Winter 2018


Optional, no required textbook:

  • ESL: The Elements of Statistical Learning (2nd Ed) by T. Hastie, R. Tibshirani and J. Friedman

  • PR: Pattern Recognition by Christopher Bishop

  • CVX: Convex Optimization by Boyd and Vandenberghe

  • R: Advanced R by H. Wickham

Lecture Schedule

Module 1 Introduction Slides Notes PR ch1, R ch1-3
Module 2 Matrix Decomposition, Least Squares, PCA, PCR and Ridge Penalization Slides Notes PR 3.1, ESL ch3.1-3.2 3.4.1, R ch4-5
Module 3 Convex Sets and Functions Slides Notes CVX ch2-3
Module 4 Optimization Basics Slides Notes CVX ch4
Module 5 Cross Validation Notes ESL ch 7
Module 6 Gradient Descent Slides Notes CVX ch 9.1-9.4


Final Project


  1. Google PageRank Algorithm

  2. Polynomial Curve Fitting and Ridge Regression

  3. Cross Validation