This Module is taught by the Department of Statistics and takes you through following topics: Basic Concepts, Markov Chains and Processes, Bayesian Statistics, Markov Chain Monte Carlo, Generalized Linear Modelling, Hidden Markov Models, Model Choice and Markov Random Fields. The first 8 days are generally organised in 4 groups of 90 minutes to 1. Overview Lecture, 2. Exercise, 3. Practical and 4. Hands-on projects. 1 or 2 lecturers have departed slightly from this format. Lectures and exercises covers models and algorithms, while Practicals and Projects are more application oriented. The 9th day has 3 research lectures in the morning and the afternoon is dedicated to project polishing. The last day has student presentations and discussions. The students are assigned projects in advance to work on 90 minutes of each afternoon typically in groups of 3. The projects illustrate key principles of statistical inference and since all projects are discussed by all, there should be some additional cross learning from this activity.