The aim of this course is two-fold: to convey the basic principles of Bayesian machine learning and to describe a practical implementation framework. Firstly, we will give an introduction to Bayesian approaches, focussing on the advantages of probabilistic modelling, the concept of priors, and the key principle of marginalisation. Secondly, we will exploit these ideas to realise practical algorithms for sparse linear regression and classification, as exemplified by models such as the "relevance vector machine".