**Here are some
resources**** for Stat 502 Spring
2018
**

Some Class "Handouts"
(Exposition)

2-Class Classification

Gram-Schmidt and QR

Two Kernel Arguments and Implicit Sets of Features

More Simple Generalities About Predictive Analytics

Ridge and Lasso Cartoons

Smoothing

Ensembles/Combining Predictors

A Support Vector Classifier Example

SVMs

Another SVM Handout

Binomial Deviance and Classification

Neural Networks and Classification

An Example of Making Good Quantitative Features for Classification from Categorical Variables

Some Class "Examples" (Computing)

SVD Example(R code)

Kernel PCA Example(R code)

Ridge/Lasso/Elastic Net

Elastic Net Regions

PCR/PLS

Wavelets

Natural Cubic Splines

MARS

Smoothing (gam)

Neural Nets (JMP)

MARS/TPS/Kernel Smoothers/Neural Nets/Averages of Neural Nets

Regression Trees

SVM

Hierarchical Clustering

Clustering

Model-Based Clustering

A PrioriAlgorithm

Materials from the 2016 Version of the Course

2016 Course Details

Homework

Data Set for Problem 5, HW1

Notes and Slides on Statistical Learning

A PhD-Level Statistical Learning Course Page

2016 Exams

Exam 1

Exam 1 Key

Exam2

Exam 2 Key

Materials from the 2014 Version of the Course

Homework

2014 Exams

Exam 1

Exam 1 Key

Exam2

Exam 2 Key