The lecture will introduce the PAC Bayes approach to the statistical analysis of learning. After some historical introduction, the key theorems will be covered. We will then consider some applications including for Support Vector Machines and novelty detection. A discussion of the status of the prior in the approach will lead to an investigation of how learning the prior can be used in practical applications. Discussions of further extensions of the approach will conclude the presentation.