Infer.NET

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• John F. Bronskill
• Thursday 19 October 2017, 13:30-15:00
• Engineering Department, CBL Seminar Room 4-38.

If you have a question about this talk, please contact Alessandro Davide Ialongo.

Abstract:

Infer.NET is a framework for running Bayesian inference in graphical models. This talk will serve as an introduction to and tutorial on using the framework. Advantages and limitations of Infer.NET will be discussed. Examples of real-world, high scale models such as skill ranking and recommendation will be demonstrated.

Reading:

No advance reading is required, but if you want to get a head start you can take a look at:

Infer.NET Website: http://infernet.azurewebsites.net/

Model-Based Machine Learning Book: http://www.mbmlbook.com/

This talk is part of the Machine Learning Reading Group @ CUED series.

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