Direct Study

Please find the notes for direct study below

Lecture

• L1: Introduction to Cross-Validation [Lec 1]

  supplementary:

  1. Tutorial: K Fold Cross Validation.

• L2: Introduction to resampling methods [TBA]

Lab

• Lab1: Will be posted soon.

Group work

We will do a Kaggle competition by the end of the course. Stay tune!

Reference

1. Hastie, T., Tibshirani R.J. and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York.

2. James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer, New York.

3. James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J. (2023). Introduction to Statistical Learning with Applications in Python. Springer, New York.

4. Bates, S., Hastie, T. and Tibshirani, R. (2023). Cross-Validation: What Does It Estimate and How Well Does It Do It? Journal of American Statistical Association, 119, 1434–1445.

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