Shelves

The mathematics underneath machine learning, in two parts: first the foundations (linear algebra, analytic geometry, matrix decompositions, vector calculus, probability and distributions, continuous optimisation), then four central problems built on them (linear regression, principal component analysis, Gaussian mixture models and support vector machines).

Notes to come as I work through it.