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Monday, December 9 • 2:45pm - 3:40pm
(SLIDES) M. Brandewinder--F# and Machine Learning: a winning combination

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While Machine Learning practitioners routinely use a wide range of tools and languages, C# is conspicuously absent from that arsenal. Is .NET inadequate for Machine Learning?  In this talk, I'll argue that it can be a great fit, as long as you use the right language for the job, namely F#.

F# is a functional-first language, with a concise and expressive syntax that will feel familiar to data scientists used to Python or Matlab. It combines the performance and maintainability benefits of statically typed languages, with the flexibility of Type Providers, a unique mechanism that enables seamless consumption of virtually any data source. And as a first-class .NET citizen, it interops smoothly with C#. So if you are interested in a language that can handle both flexible data exploration and the pressure of a real production system, come check out what F# has to offer!



Speakers
avatar for Mathias Brandewinder

Mathias Brandewinder

Mathias Brandewinder has been writing software for about 10 years, primarily in C# until he fell in love with F# and functional programming. He enjoys arguing about code and how to make it better, and gets very excited when discussing TDD or F#. His other professional interests are applied math, predictive models and machine learning.



Monday December 9, 2013 2:45pm - 3:40pm
Hall 5.2

Attendees (24)