Lectures

Stat 154/254: Statistical Machine Learning

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Lectures marked with \(\star\) are still in progress.

Introduction

Class introduction

Unit 1: Regression in a machine learning setting

• Linear regression in matrix notation (review)
• Linear regression in an ML setting
• Basis expansions and splines for linear regression
• Asymptotics and bias–variance tradeoff in linear regression
• L1 and L2 penalization

Unit 2: Risk and complexity

• \(\star\) Held-out data and cross-validation
• \(\star\) Generalization and uniform laws
• \(\star\) Bayesian estimators and Bayes risk

Unit 3: Classification

• \(\star\) Discriminative and generative classification
• \(\star\) Logistic regression asymptotics
• \(\star\) False positives and false negatives
• \(\star\) Naive Bayes and density estimation
• \(\star\) Support vector machines

Unit 4: Unsupervised learning

TBD

Unit 5: Computation

TBD

Unit 6: Forming more expressive models

TBD

Extra content

• Linear algebra review materials
• \(\star\) Asymptotics notes