Lectures 1: Introduction to machine learning and data mining. PDF

Lectures 2: Learning, data and Google's search engine. PDF

Lectures 2b, 3, and 4: Linear algebra and the SVD. Application to data visualisation, semantic text search engines and image compression. PDF

Lectures 5 and 6: Linear supervised learning and ridge regression. PDF

Lecture 7: Nonlinear regression and kernel methods. PDF

Lectures 8 and 9: Classification, SVMs, unsupervised learning, k-means clustering, EM, mixtures of Gaussians, naive Bayes. PDF