Lecture slidesΒΆ

Here are the slides that I used in the video lectures.

  1. Prediction vs inference

  2. Prediction routine + Bias-Variance trade-off

  3. Test vs Cross-validation error

  4. Decision trees + pruning

  5. Classification trees

  6. Bagging, Random Forest, and Boosting

  7. Double selection + Why naive ML fails

  8. Double Machine Learning

  9. Generalized Random Forest

For your convenience, I put all the slides together in the same repo with the tutorials, so you can download them in bulk.

Here are the slides for Friday Q&A sessions:

  1. Week 1

  2. Week 2

  3. Week 3

Textbooks and papers