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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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By Sergio Bacallado, Jonathan Taylor (following James et al. ISLR 2nd edition)
© Copyright 2022.