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Machine Learning at MSR Cambridge
Machine Learning at MSR Cambridge

Machine learning is one of the major research themes in the Machine Intelligence & Perception group in Cambridge. We develop new machine learning methods, taking them from theory to generic computer implementations. We also work on practical applications of these new methods, as well as more established methods in new domains.


  • Infer.NET 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.
  • 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 Intelligence and Perception
> Machine Learning at MSR Cambridge