Syllabus

Slides

Chapter 2: Statistical Learning- pdf (part 1, part 2), ppt (part 1, part 2)

Chapter 3: Linear Regression- pdf, ppt

Chapter 4: Classification- pdf (part 1, part 2), ppt (part 1, part 2)

Chapter 5: Resampling Methods- pdf, ppt

Chapter 6: Linear Model Selection and Regularization- pdf, ppt

Chapter 7: Moving Beyond Linearity

Chapter 8: Tree-Based Methods- pdf (part 1, part 2), ppt (part 1, part 2)

Chapter 9: Support Vector Machines- pdf, ppt

Chapter 10: Unsupervised Learning- pdf, ppt

R Code with Explanations

KNN for Classification: pdf

Simple Linear Regression: html, pdf

Multiple Linear Regression: html, pdf

Logistic Regression, LDA, QDA: pdf

(Similar documents will be added during the Fall 2014 semester)

Project

Project Description: Download

Sample 1: What Causes Retweets?

Sample 2: Purchasing Lemons at an Auction

Sample 3: Factors Driving GDP Growth

Sample 4: Finding Happiness

Sample 5: Donor Trends for a Non-Profit Organization

Sample 6: Breaking Vegas

Sample 7: Analyzing Biochemical Properties to Grade Wine Quality

Sample 8: Superball Predictions

Sample 9: How Could High School Prospects Increase their Chances at NFL Success?

Sample 10: Analysis of Determinants of Peer-to-Peer Load Default

Sample 11: SPAM Classification

Book

"An Introduction to Statistical Learning with Applications in R” by James, Witten, Hastie, and Tibshirani.

###### R Video Casts

Introduction to R

Writing R Functions

Linear Regression in R

Logistic Regression in R

LDA, QDA, and KNN in R

Cross Validation in R

Decision Trees in R (Classification)

Decision Trees in R (Regression)