- A summary of notation from the course, and a review of least squares regression is available here.

- The Elements of Statistical Learning (T. Hastie, R. Tibshirani and J. Friedman) has excellent background material for large parts of this course, presented in a less mathematical style.

- Understanding Machine Learning: From Theory to Algorithms (S. Shalev-Shwartz and S. Ben-David) covers much of our course and a lot more.

- MIT lecture notes (P. Rigollet) - Part II is particularly good for the part of our course on computation and optimisation.

- Stanford lecture notes (P. Liang) - Chapter 3 is great for the part of our course on statistical learning theory.

- High-Dimensional Statistics: A Non-Asymptotic Viewpoint (M. Wainwright) is highly recommended if you want to learn more about theory for modern machine learning and statistics. Chapters 2 and 4 are particularly relevant for our course..

The code for the demonstrations is written in R. Rstudio is a useful editor for R. Here are some introductory worksheets on R: Sheet 1, (solutions); Sheet 2, (solutions). The code for the demonstrations will be given below.

- Lecture 15, Adaboost, small example as well as larger example.

Below are the example sheets and revision questions for the course.

Note to supervisors: Append "_sol" to the link addresses below to obtain solutions (email rds37@cam.ac.uk to obtain the password).

- Example Sheet 1 (based on material in lectures 1-6).

- Example Sheet 2 (based on material up to lecture 11).

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