Ham Or Spam - Hands on Machine Learning with Mathias Brandewinder

Dec 17, 2013 · London, United Kingdom

Machine Learning is the art of writing programs that get better at performing a task as they gain experience, without being explicitly programmed to do so. Feed your program more data, and it will get smarter at handling new situations. 

In this session, we'll learn about the Naive Bayes classifier, and take a classic Machine Learning problem, recognizing Spam from Ham. 

This session will be organized as an interactive workshop. Come over, write some code, and learn yourself a F# and Machine Learning for great good! No prior experience with Machine Learning required, and F# beginners are very welcome - it will be a great opportunity to see F# in action, and why it's awesome.

To get the most from the session, please try and bring a laptop along with F# installed (ideally either MonoDevelop or Visual Studio Web Express/Full Edition). Check out fsharp.org for instructions on how to get F# ready on your machine.

Mathias Brandewinder has been writing software in C# for about 10 years, and loving every minute of it, except maybe for a few release days. He is an F# MVP, 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 and probability. If you want to know more about him, you can check out his blog at www.clear-lines.com/blog or find him on Twitter as@brandewinder.

Please register with Skills Matter too: http://skillsmatter.com/event-details/home/ham-or-spam-hands-on-machine-learning-with-mathias-brandewinder


Event organizers
  • F#unctional Londoners Meetup Group

    F#unctional Londoners is a meetup group that aims to bring together Londoners with an interest in functional programming with F#. Topics include: - Essentials of functional programming and F# - Assessing what kind of projects F# is well suited for - or not - Using F# alongside other languages and libraries - Asynchronous and parallel programming - Data visualisation - Scientific programming - Numerical methods and optimisation - Financial modelling

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