Resources
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A summary of notation from the course, and a review of least squares regression is available here.
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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.
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MIT lecture notes (P. Rigollet) - Part II is particularly good for the part of our course on computation and optimisation.
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Stanford lecture notes (P. Liang) - Chapter 3 is great for the part of our course on statistical learning theory.
Code for Demonstrations
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.
Example Sheets
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).
