Machine Learning

Christopher Bishop, Ralf Herbrich, Thore Graepel, Anitha Kannan, Tom Minka, Martin Szummer, Markus Svensén, John Winn

Our Research

Our current research covers a broad range of topics. Examples are:

Message Passing Methods

Novel numerical algorithms for computing probabilities and parameter estimates in Bayesian networks.

Bioinformatics

We develop new methods for genome-level analysis of DNA data to discover genetic factors behind common diseases.

Infer.NET

A comprehensive library of statistical algorithms for the .NET platform with a dedicated programming language.

All Topics

CSOFT
Dynamic Bayesian Networks and their application to Failure Analysis
Infer.NET
Kernel Methods
Learning Theory
Message Passing Methods
Online Ink Analysis
The Relevance Vector Machine

Scientific publications

Here is a list of recent publications.

Links

Collaborators: Geoffrey Hinton, Chris Williams
Former members: Julian Gold, Mark Hatton, Neil Lawrence, Mike Tipping.
Friends: The Inference Group

We are part of the Machine Learning and Perception group at Microsoft Research Ltd, Cambridge, UK.