Statistics 502X
 

Here are some resources for Stat 502X Spring 2016:

Syllabus

2016 Course Details (as they develop)

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
 
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 Priori Algorithm


Materials from the 2014 Version of the Course

Syllabus

Detailed Schedule (2014)

Summary

Homework

2014 Exams

Exam 1
Exam 1 Key
Exam2
Exam 2 Key

 


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Last updated: 1/23/2016