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STATS 202
  • Syllabus

Assignments

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Assignment 4
  • Assignment 5

Introduction

  • Prediction challenges
  • Unsupervised learning
  • Supervised learning

Linear regression

  • Simple linear regression
  • Multiple linear regression
  • \(K\) -nearest neighbors

Classification

  • Basic approach
  • Logistic regression
  • Linear Discriminant Analysis (LDA)
  • Quadratic discriminant analysis (QDA)
  • Evaluating a classification method

Resampling

  • Validation
  • Leave one out cross-validation (LOOCV)
  • \(K\) -fold cross-validation
  • Bootstrap

Model selection

  • Best subset selection
  • Stepwise selection methods
  • Shrinkage methods
  • Dimensionality reduction
  • High-dimensional regression

Nonlinear methods

  • Basis expansions
  • Splines
  • Local linear regression
  • Generalized Additive Models (GAMs)

Tree-based methods

  • Regression trees
  • Classification trees
  • Some details
  • Bagging
  • Boosting

Support vector machines

  • Margins and separating hyperplanes
  • Maximal margin classifier
  • Support vector classifier
  • Kernels and support vector machines

Unsupervised methods

  • Principal Components Analysis
  • Clustering
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Contents

Contents

By Sergio Bacallado, Jonathan Taylor (following James et al. ISLR 2nd edition)
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