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My notes, labs, solutions, etc for Introduction to Statistical Learning - ISLR
Much of the information is in a Github repo

• Chapter 02 Notes -
  • Lab 2 - Introduction to R
  • Applied Exercies
• Chapter 03 Notes - Linear Regression
  • Lab 3 - Linear Regression
  • Conceptual
  • Applied
  • Question 8 - Simple Linear Regression/Auto
  • Question 9 - Multiple Linear Regression/Auto
  • Question 10 - Multiple Regression/Carseats
  • Question 11 - t-statistic for the null hypothesis
  • Question 12 - Simple linear regression w/o intercept
  • Question 13 - Simple Linear Regression with Simulated Data
  • Question 14 - Collinearity Problem
  • Question 15 - Predict per capita crime rate/Boston
• Chapter 04 Notes - Classification
  • Lab 4 - Logistic Regression, LDA, QDA, and KNN
  • Lab 4 - Caravan
  • Conceptual
  • Question 6 - Predict Student Grade
  • Question 7 - Predict Stock Issues Dividend
  • Question 8 - TODO
  • Question 9 - Odds
  • Applied
  • Question 10 - Logistic Regression, LDA, QDA, KNN/Weekly
  • Question 11 - Predict High or Low mpg/Auto
  • Question 12 - Power functions
  • Question 13 - TODO
• Chapter 05 Notes - Resampling Methods
  • Lab 5 -
  • Conceptual
  • Question 2 - Bootstrap
  • Question 5 - TODO
  • Applied
  • Question 6 - GLM - TODO /Default
  • Question 7 - cv.glm(), LOOCV /Weekly
  • Question 8 - Cross-Validation on Simulated Data
  • Question 9 - /Boston
• Chapter 06 Notes - Linear Model Selection and Regularization
  • Lab 6 -
  • Question 8 - Best Subset Selection
  • Question 9 - Predict the number of applications received/College
  • Question 10 - TODO
  • Question 11 - Crime Rate/Boston
• Chapter 07 Notes - Moving Beyond Linearity
  • Lab 7 -
  • Question 6 - Predict Wage/Wage
  • Question 7 - Non-linear Fitting Techniques/Wage
  • Question 8 - Non-Linear Models/Auto
  • Question 9 - Predict dis /Boston
  • Question 10 TODO
  • Question 11 TODO
  • Question 12 TODO
• Chapter 08 Notes - Tree-Based Methods
  • Lab 8 - Lab 1
  • Lab 8 - Lab 2
  • Lab 8 - Lab 3
  • Question 7 - Random Forest/Boston
  • Question 8 - TODO
  • Question 9 - Regression Trees/OJ
  • Question 10 - Random Forest - Boosting/Hitters
  • Question 11 - Boosting/Caravan
  • Question 12 - Boosting, Bagging, and Random Forests
• Chapter 09 Notes - Support Vector Machines
  • Lab 9 - Support Vector Machines
  • Question 4
  • Question 5 - SVM with a non-linear kernel
  • Question 6 - SVMs with a range of cost values
  • Question 7 - Support Vector/Auto
  • Question 8 - Support Vector/OJ
• Chapter 10 Notes - Unsupervised Learning
  • Lab 10 - Lab 1 - Principal Components Analysis
  • Lab 10 - Lab 2 - Clustering
  • Lab 10 - Lab 3 - NCI60 Data Example
  • Question 7 - Hierarchical Clustering/USArrests
  • Question 8 - Principal Components
  • Question 9 - Hierarchical Clustering on States/USArrests
  • Question 10 - PCA and K-means clustering
  • Question 11 - Hierarchical Clustering on Gene Expression Data