Teaching - Mathematics of Machine Learning

Printed notes.

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..

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

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

Teaching - Mathematics of Machine Learning

Resources

Code for Demonstrations

Example Sheets

Comments and Questions