Here are some resources for Stat 602X:
Slides (A Slightly Condensed Version of the Outline)
Module 1 (Introduction)
Module 2 (Decision Theory, Variance-Bias Tradeoff, Cross-Validation)
Module 3 (Some Linear Theory)
Module 4 (Basic Principal Components)
Module 5 (Non-OLS Linear Predictors Part 1)
Module 6 (Non-OLS Linear Predictors Part 2)
Module 7 (Non-OLS Linear Predictors Part 3)
Module 8 (Linear Prediction Using Basis Functions Part 1)
Module 9 (Linear Prediction Using Basis Functions Part 2)
Module 10 (Linear Prediction Using Basis Functions Part 3)
Module 11 (Smoothing Splines in 1-D)
Module 11A (Penalized Fitting in R^N)
Module 12 (Multi-Dimensional Smoothing Splines)
Module 13 (Kernel Smoothing Methods)
Module 14 (High-Dimensional Use of Low-Dimensional Smoothers)
Module 15 (Highly Flexible Non-Linear Parametric Regression Methods)
Module 16 (Trees and Related Methods Part 1)
Module 17 (Trees and Related Methods Part 2)
Module 18 (Trees and Related Methods Part 3)
Module 19 (Predictors Built on Bootstrap Samples)
Module 20 (Model Averaging and Stacking)
Module 21 (RKHSs and Penalized/Regularized Fitting Part 1)
Module 22 (RKHSs and Penalized/Regularized Fitting Part 2)
Module 23 (RKHSs and Penalized/Regularized Fitting Part 3)
Module 24 (RKHSs and Penalized/Regularized Fitting Part 4)
Module 25 (Understanding and Predicting Predictor Performance Part 1)
Module 26 (Understanding and Predicting Predictor Performance Part 2)
Module 27 (Generalities About Classification)
Module 28 (Linear Methods of Classification Part 1)
Module 29 (Linear Methods of Classification Part 2)
Module 30 (Support Vector Machines Part 1)
Module 31 (Support Vector Machines Part 2)
Module 32 (Support Vector Machines Part 3)
Module 33 (Support Vector Machines Part 4)
Module 34 (Boosting Part 1)
Module 35 (Boosting Part 2)
Module 36 (Prototype and Nearest Neighbor Methods of Classification)
Module 37 (Association Rules/Market Basket Analysis)
Module 38 (Clustering Part 1)
Module 39 (Clustering Part 2)
Module 40 (Multi-Dimensional Scaling)
Module 41 (Spectral Clustering)
Module 42 (Variations on Principal Components)
Module 43 (Independent Component Analysis)
Module 44 (Density Estimation)
Module 45 (Google PageRanks)
Module 46 (Document Features and String Kernels for Text Processing)
Module 47 (Kernel Mechanics)
Module 48 (Undirected Graphical Models and Machine Learning)
Data
Sets for Homework
Problem 10: Set3
Problem 14: Set4
Problem 16: Set5
Problem 25: Seeds
Problem 27: Spectral
Exam 1 (Sp '13)
Key
Exam 2 (Sp '13)
Key
2011 Course Outline
Exam 1 (Sp '11) Key Exam 2 (Sp '11) Key
2011 Homework 1 Solution 2011 Homework 2 Solution 2011 Homework 3 Solution 2011 Homework 4 Solution 2011 Homework 5 Solution 2011 Homework 6 Solution
