.net vs python - Resources for working with Machine Learning in F#

2 Answers

In addition to what Tomas mentioned, I spent some time with Infer.NET about a year ago and found it was pretty good for continuous graphical models. I know it's improved quite a lot over the last year in both the scope of the library and F# support. I suggest checking it out and seeing if it has what you need.

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I have learned a Machine Learning course using Matlab as a prototyping tool. Since I got addicted to F#, I would like to continue my Machine Learning study in F#.

I may want to use F# for both prototyping and production, so a Machine Learning framework would be a great start. Otherwise, I can start with a collection of libraries:

  • Highly-optimized linear algebra library
  • Statistics package
  • Visualization library (which allows to draw and interact with charts, diagrams...)
  • Parallel computing toolbox (similar to Matlab parallel computing toolbox)

And the most important resources (to me) are books, blog posts and online courses regarding Machine Learning in a functional programming language (F#/OCaml/Haskell...).

Can anyone suggest these kinds of resource? Thanks.


This is a summary based on the answers below:

Machine Learning frameworks:

  • Infer.NET: an .NET framework for Bayesian inference in graphical models with good F# support.
  • WekaSharper: a F# wrapper around the popular data mining framework Weka.
  • Microsoft Sho: a continuous environment development for data analysis (including matrix operations, optimization and visualization) on .NET platform.

Related libraries:

Reading list:

Any other pointers or suggestions are also welcome.

APress has a book in "Alpha" slated for release soon: Machine Learning Projects for .NET Developers. http://www.apress.com/9781430267676

The currently existing content seems to be introductory, but quite good to learn from, and its code samples are primarily F#.