## Lectures

Lectures 1: Introduction to machine learning and data mining.

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

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

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

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Lecture 7: Nonlinear regression and kernel methods.

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

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