Machine Learning at MSR Cambridge
Machine Learning at MSR Cambridge

The machine learning group in Cambridge develops new machine learning methods, taking them from theory to generic computer implementations. It also works on the application of these new methods, as well as more established ones, in new domains.

Projects

  • Infer.NET our main focus, Infer.NET is a framework for automatically applying probabilistic inference to a large variety of problems.  Infer.NET has required the development of new, modular machine learning algorithms, along with new ways of architecting inference software that can deliver efficient, customised code tailored to solve particular problems. This work has also led to developments in probabilistic programming.
  • Machine learning theory – much of our theoretical research relates to deterministic approximate algorithms such as Expectation Propagation and Variational Message Passing.  More recently, our theoretical work has focused on solutions needed for the Infer.NET framework, such as new scheduling algorithms and gates.
  • Bioinformatics applications – in collaboration with the Wellcome Trust Sanger Institute we are developing new methods to understand the function of our genes and investigate how genetic variation can give rise to common human diseases.
  • Healthcare applications – electronic healthcare records contain information about the nature of disease and the effectiveness of treatments that could have enormous benefits for medicine and public health.  We are developing methods to help unlock this information from the wealth of varied and imperfect healthcare data.
  • Other machine learning applications – we work with many of the other groups from MSR Cambridge and beyond to build machine learning solutions to problems in machine vision, informational retrieval, human-computer interaction, game theory, software development and many other domains. 

Machine Learning and Perception
> Machine Learning at MSR Cambridge

Publications