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Infer.NET and probabilistic programming

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If you have a question about this talk, please contact Dr Fabien Petitcolas.

Abstract: Would you like to write software that can adapt to new situations, learn from examples or work with uncertain information? Infer.NET is a machine learning framework that lets you build such capabilities easily using a new way of programming called probabilistic programming. Probabilistic programs can work with uncertain or unknown variables and even uncertain execution. By using such programs, you can combine detailed domain knowledge with the latest machine learning algorithms to generate tailored code to solve your problem. I’ll explain what probabilistic programming is and give some example of using it for search and for online gaming.

Biography: John Winn is a researcher in the machine learning group at MSR Cambridge. He did his doctorate on inference frameworks and has also been working on Infer.NET since its inception. At MSR , he has worked on applications ranging from recognising Bill Gates’ glasses in a live video to understanding the genetic causes of asthma. His goal is to make powerful machine learning techniques accessible to everyone.

This talk is part of the Microsoft Research Summer School series.

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