STAT 140B Statisical Learning

Sample Syllabus

Slides

The following slides’ content is borrowed/adapted from the textbook Introduction to Statistical Learning and associated slides by James, Witten, Hastie, And Tibshirani.

Chapter 2: Statistical Learning

• 2.1 What is Statistical Learning?
• 2.2 Assessing Model Accuracy

Chapter 3: Linear Regression

• 3.1 (Simple) Linear Regression
• 3.2 Multiple Linear Regression
• 3.3 Other Considerations in the Regression Model

Chapter 4: Classification

• 4.1 and 4.2 Introducing Classification
• 4.3 Logistic Regression
• 4.4 Generative Models for Classification
  • 4.4.1 Linear Discriminant Analysis for p = 1
  • 4.4.2 Linear Discriminant Analysis for p > 1
  • 4.4.3 Quadratic Discriminant Analysis
  • 4.4.4 Naive Bayes
• 4.5 A Comparison of Classification Methods
  • Note that these are not directly based on the ISLR text; students are recommended to browse that section on their own.
• 4.6 Generalized Linear Models

Chapter 5: Resampling Methods

• 5.1 Cross-Validation
• 5.2 Bootstrap
  • (Thanks to Dr. James Flegal, UC Riverside, for some of the content in these!)

Chapter 6: Linear Model Selection and Regularization

• 6.1 Subset Selection
• 6.2 Shrinkage Methods
• 6.3 Dimension Reduction Methods
• 6.4 Considerations in High Dimensions

Chapter 7: Linear Model Selection and Regularization

• 7.1 Polynomial Regression (covered in detail in Stat 140A)
• 7.2 Step Functions
• 7.3 and 7.4: Basis Functions and Regression Splines