An Introduction to Statistical Learning with Applications in Python

Preface

Introduction

• An Overview of Statistical Learning
• A Brief History of Statistical Learning
• This Course
• Who Should Read This Course?
• Notation and Simple Matrix Algebra
• Organization of This Course
• Data Sets Used in Labs and Exercises

Statistical Learning

• What is Statistical Learning?
• Assessing Model Accuracy
• Lab: Introduction to Python
• Exercises

Linear Regression

• Simple Linear Regression
• Multiple Linear Regression
• Other Considerations in the Regression Model
• The Marketing Plan
• Comparison of Linear Regression with K-Nearest Neighbors
• Lab: Linear Regression
• Exercises

Classification

• An Overview of Classification
• Why Not Linear Regression?
• Logistic Regression
• Generative Models for Classification
• A Comparison of Classification Methods
• Generalized Linear Models
• Lab: Logistic Regression, LDA, QDA, and KNN
• Exercises

Resampling Methods

• Cross-Validation
• The Bootstrap
• Lab: Cross-Validation and the Bootstrap
• Exercises

Linear Model Selection and Regularization

• Subnet Selection
• Shrinkage Methods
• Dimension Reduction Methods
• Considerations in High Dimensions
• Lab: Linear Models and Regularization Methods
• Exercises

Moving Beyond Linearity

• Polynomial Regression
• Step Functions
• Basis Functions
• Regression Splines
• Smoothing Splines
• Local Regression
• Generalized Additive Models
• Lab: Non-Linear Modeling
• Exercises

Tree-Based Methods

• The Basics of Decision Trees
• Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees
• Lab: Tree-Based Methods
• Exercises

Support Vector Machines

• Maximal Margin Classifier
• Support Vector Classifiers
• Support Vector Machines
• SVMs with More than Two Classes
• Relationship to Logistic Regression
• Lab: Support Vector Machines
• Exercises

Deep Learning

• Single Layer Neural Networks
• Multilayer Neural Networks
• Convolutional Neural Networks
• Document Classification
• Recurrent Neural Networks
• When to Use Deep Learning
• Fitting a Neural Network
• Interpolation and Double Descent
• Lab: Deep Learning
• Exercises

Survival Analysis and Censored Data

• Survival and Censoring Times
• A Closer Look at Censoring
• The Kaplan-Meier Survival Curve
• The Log-Rank Test
• Regression Models With a Survival Response
• Shrinkage for the Cox Model
• Additional Topics
• Lab: Survival Analysis
• Exercises

Unsupervised Learning

• The Challenge of Unsupervised Learning
• Principal Components Analysis
• Missing Values and Matrix Completion
• Clustering Methods
• Lab: Unsupervised Learning
• Exercises

Multiple Testing

• A Quick Review of Hypothesis Testing
• The Challenge of Multiple Testing
• The Family-Wise Error Rate
• The False Discovery Rate
• A Re-Sampling Approach to p-Values and False Discovery Rates
• Lab: Multiple Testing
• Exercises