Stanford MS&E 226 – Fundamentals of Data Science

Lecture Notes

• 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

Additional Online Course Video Lecture Notes

• Model scores

• Model selection using model scores

• Logistic regression

• Maximum likelihood

• F test

• Experiment design

• Instrumental variables