Resources for working with Machine Learning in F#

Question

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 resources? Thanks.

Answer 1

There are many resources on F# and machine learning available online, but here are some links to a few of them that may be useful:

• Numerical Computing section on MSDN is a good resource on using various numerical libraries from F#. The most advanced library that implements linear algebra and other algorithms useful in machine learning is Math.NET Numerics.

• Visualizing Data section on MSDN has some resources on charting in F#. The FSharpChart library is now maintained by Carl Nolan who regularly posts updates to his blog.

There are also a few personal pages of people who are working on relevant topics:

• Jurgen van Gael contributed to the Math.NET library and you can read about his experience here.

• Yin Zhu who wrote the Numerical Computing chapter on MSDN (and is a Ph.D. student interested in machine learning) has quite a few excellent articles on his blog.

I hope this will be quite useful.