2013-03-11

Machine learning libraries to use for Project Miracle

For the machine learning portion of Project Miracle, I may end up trying several different languages and libraries to see which works best. This will also give me a chance to learn more about the various algorithms and approaches. Here are a few I’m considering:

• R and related libraries: I’ve learned about R through Coursera’s courses Computing for Data Analysis and Data Analysis; I think I have one or more books on R lying around somewhere in either paper or ebook form, but I can’t seem to find them at the moment.

• Mahout: Familiar with this one because of its usage within the Java and Hadoop communities.

• Octave/Matlab: Familiar with this language/environment through Andrew Ng’s machine learning course from both Coursera and iTunes U.

• Breeze from scalanlp.org: My friend Devon is using this library for his Master’s thesis. I’m interested in using Scala, so may have to learn more about this library.

• Weka: Another general purpose machine learning library written in Java. Also appears to be used by the folks at Pentaho.

• MLC++: A machine learning library written in C++ at Stanford; now distributed by SGI.

• Python and related machine learning libraries. Need to research the best options available in this environment.

Any other suggestions?

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