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)