Lecture 1: Introduction

Lecture 2: Linear regression (annotated)

Lecture 3: More on linear regression (annotated)

Lecture 4: Introduction to prediction (annotated)

code for model selection examples

Lecture 5: In-sample estimation of prediction error (annotated)

code for cross validation

Lecture 6: Bias and variance (annotated)

Lecture 7: Classification (annotated)

code for CORIS data example

Lecture 8: Introduction to inference

Lecture 9: Frequentist properties of estimators (annotated)

Lecture 10: The bootstrap (annotated)

Lecture 11: Hypothesis testing (annotated)

Lecture 12: Bayesian inference (annotated)

Lecture 13: Additional thoughts on inference

Lecture 14: Introduction to causal inference

Lecture 15: Additional topics in causal inference

Model scores

Model selection using model scores

Logistic regression

Maximum likelihood

F test

Experiment design

Instrumental variables