EE104/CME107: Introduction to Machine Learning

Stanford University, Spring Quarter, 2024

Lecture slides

These are the lecture notes from last year. Updated versions will be posted during the quarter.

  1. Course information

  2. Overview and examples

  3. Predictors

  4. Validation

  5. Features

  6. Empirical risk minimization

  7. Constant predictors

  8. House prices example and house.jl, houseplots.jl, house.csv

  9. Non-quadratic losses

  10. Non-quadratic regularizers

  11. Neural networks

  12. Classifiers

  13. ERM for classifiers

  14. Boolean classification

  15. Multi-class classification

  16. Probabilistic classification

  17. ERM for probabilistic classification

  18. Unsupervised learning

  19. Principal components analysis

  20. Optimization

  21. Prox-gradient method

Supplemental notes

These notes will not be covered in the lecture videos, but you should read these in addition to the notes above.

  1. Notation