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POResources for working with Machine Learning in F#
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copied!<p>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#. </p> <p>I may want to use F# for both prototyping and production, so <strong>a Machine Learning framework</strong> would be a great start. Otherwise, I can start with a collection of libraries:</p> <ul> <li>Highly-optimized linear algebra library</li> <li>Statistics package</li> <li>Visualization library (which allows to draw and interact with charts, diagrams...)</li> <li>Parallel computing toolbox (similar to Matlab parallel computing toolbox)</li> </ul> <p>And the most important resources (to me) are <strong>books</strong>, blog posts and online courses regarding Machine Learning in a functional programming language (F#/OCaml/Haskell...). </p> <p>Can anyone suggest these kinds of resource? Thanks.</p> <hr> <p><strong>EDIT:</strong></p> <p>This is a summary based on the answers below:</p> <p>Machine Learning frameworks:</p> <ul> <li><a href="http://research.microsoft.com/en-us/um/cambridge/projects/infernet/">Infer.NET</a>: an .NET framework for Bayesian inference in graphical models with good F# support.</li> <li><a href="http://wekasharp.codeplex.com/">WekaSharper</a>: a F# wrapper around the popular data mining framework Weka.</li> <li><a href="http://msdn.microsoft.com/en-us/library/hh304371.aspx">Microsoft Sho</a>: a continuous environment development for data analysis (including matrix operations, optimization and visualization) on .NET platform.</li> </ul> <p>Related libraries:</p> <ul> <li><p><a href="http://mathnetnumerics.codeplex.com/">Math.NET Numerics</a>: internally using Intel MKL and AMD ACML for matrix operations and supporting statistics functions too. </p></li> <li><p><a href="http://archive.msdn.microsoft.com/solverfoundation">Microsoft Solver Foundation</a>: a good framework for linear programming and optimization tasks.</p></li> <li><p><a href="http://code.msdn.microsoft.com/windowsdesktop/FSharpChart-b59073f5">FSharpChart</a>: a nice data visualization library in F#.</p></li> </ul> <p>Reading list:</p> <ul> <li><a href="http://msdn.microsoft.com/en-us/library/hh273075.aspx">Numerical Computing</a>: It is great for starting with Machine Learning in F# and introduces various tools and tips/tricks for working with these Math libraries in F#.</li> <li><a href="http://fdatamining.blogspot.com/">F# and Data Mining blog</a>: It is also from Yin Zhu, the author of Numerical Computing chapter, highly recommended.</li> <li><a href="http://blog.codebeside.org/blog/2011/10/27/f-as-a-octavematlab-replacement-for-machine-learning">F# as a Octave/Matlab replacement for Machine Learning</a>: Gustavo has just started a series of blog posts using F# as the development tool. It's great to see many libraries are plugged in together.</li> <li><a href="http://www.clear-lines.com/blog/?tag=/Machine%20Learning">"Machine Learning in Action" 's samples in F#</a>: Mathias has translated some samples from Python to F#. They are available in <a href="https://github.com/mathias-brandewinder/Machine-Learning-In-Action">Github</a>.</li> <li><a href="http://www.umiacs.umd.edu/~hal/software.html">Hal Daume's homepage</a>: Hal has written a number of Machine Learning libraries in OCaml. You would feel relieved if you were in doubt that functional programming was not suitable for Machine Learning.</li> </ul> <p>Any other pointers or suggestions are also welcome.</p>

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