2017/18

Imperial College London (CO-493) The course is co-taught by Duncan Gillies and Marc Deisenroth. Syllabus Bayes’ Theorem and Bayesian Inference Bayesian Decision Trees -Evidence and message passing Inference in singly connected networks Building networks from data Cause and Independence Model Accuracy Approximate Inference Exact Inference Probability propagation in Join Trees Graphical models Gaussian processes Distributed Gaussian processes Bayesian optimization MCMC

Imperial College London (CO-496) The aim of the course is to provide the students the necessary mathematical background and skills in order to understand, design and implement modern statistical machine learning methodologies, as well as inference mechanisms.