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Machine learning-with-f sharp

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  • 1. © Mathias Brandewinder / @brandewinder Machine Learning on .NET F# FTW!
  • 2. © Mathias Brandewinder / @brandewinder A few words about me »Mathias Brandewinder / @brandewinder »Background: economics, operations research ».NET developer for 10~ years (C#, F#) »Bay.Net San Francisco, SFSharp.org »www.clear-lines.com/blog
  • 3. © Mathias Brandewinder / @brandewinder I am assuming… »Few familiar with F# »Mostly unfamiliar with Data Science / Machine Learning »Mostly familiar with OO languages (C#, Java) »Some familiar with Functional Languages
  • 4. © Mathias Brandewinder / @brandewinder Why this talk »Machine Learning, Data Science are red-hot topics › ... and relevant to developers ».NET is under-represented »ML is also for developers!
  • 5. © Mathias Brandewinder / @brandewinder My goal »Can’t introduce F#, Machine Learning under 1h »Give you a sense for what Machine Learning is › Highlight some differences with “standard” development › Mostly live code »Illustrate why I think F# is a great fit
  • 6. © Mathias Brandewinder / @brandewinder What is F#? »Functional first, statically typed language »Cross-platform: Windows, iOS, Linux »Open-source (www.github.com/fsharp) »Think lighter Scala? Python with types? »Very friendly community (Twitter #fsharp)
  • 7. © Mathias Brandewinder / @brandewinder What is Machine Learning? »"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E“ [Tom M. Mitchell]
  • 8. © Mathias Brandewinder / @brandewinder In English, please? »Program performs a Task using Data »The more Data, the better it gets »Rooted in statistics, math »A Computer Science problem as well › Used in live software, with changing data
  • 9. © Mathias Brandewinder / @brandewinder The plan »Classification »Regression »Unsupervised »Type Providers »Existing .NET libraries »Algebra »Functional fit
  • 10. © Mathias Brandewinder / @brandewinder Classification & Regression
  • 11. © Mathias Brandewinder / @brandewinder Goal »What does “a day of Machine Learning” look like? »Illustrate Classification and Regression
  • 12. © Mathias Brandewinder / @brandewinder Classification, Regression »Classification = using data to classify items › Ex: Spam vs. Ham, Character Recognition, … »Regression = predicting a number › Ex: predict price of item given attributes, … »Both belong to Supervised Learning › You know what question you are trying to answer › You use data to fit a predictive model
  • 13. © Mathias Brandewinder / @brandewinder Support Vector Machine »Classic algorithm »Tries to separate the 2 classes by the widest possible margin »Using Accord.NET implementation
  • 14. © Mathias Brandewinder / @brandewinder Demo: Kaggle Digit Recognizer
  • 15. © Mathias Brandewinder / @brandewinder Take-Aways »F# is a first-class citizen in .NET »Good libraries: Accord.NET, Math.NET, Alea.cuBase, … »Interactive experience with the REPL »Syntax matters! »Classification, Regression, Cross-Validation
  • 16. © Mathias Brandewinder / @brandewinder Unsupervised
  • 17. © Mathias Brandewinder / @brandewinder Goal »Illustrate unsupervised learning »Functional programming and ML are a great fit
  • 18. © Mathias Brandewinder / @brandewinder Writing your own »Usually not advised »Useful for ML because › Active research: you might not have a library yet › As you learn your domain, you may need a custom model
  • 19. © Mathias Brandewinder / @brandewinder Most ML algorithms are the same »Read data »Transform into Features »Learn a Model from the Features »Evaluate Model quality
  • 20. © Mathias Brandewinder / @brandewinder Translates well to FP »Read data »Transform into Features -> Map »Learn a Model from the Features -> Recursion »Evaluate Model quality -> Fold/Reduce
  • 21. © Mathias Brandewinder / @brandewinder Focus on transforms, not objects »Need to transform rapidly Features › Don’t force domain to fit algorithm › Morph around the shape of the data, pass functions › Algorithms need to be generic »FP is fantastic for code reuse
  • 22. © Mathias Brandewinder / @brandewinder What is Unsupervised Learning? »“Tell me something about my data” »Example: Clustering › Find groups of “similar” entities in my dataset
  • 23. © Mathias Brandewinder / @brandewinder Example: clustering (1)
  • 24. © Mathias Brandewinder / @brandewinder Example: clustering (2) “Assign to closest Centroid” [Map Distance]
  • 25. © Mathias Brandewinder / @brandewinder Example: clustering (3) “Update Centroids based on Cluster” [Reduce]
  • 26. © Mathias Brandewinder / @brandewinder Example: clustering (4) “Stop when no change” [Recursion]
  • 27. © Mathias Brandewinder / @brandewinder Demo
  • 28. © Mathias Brandewinder / @brandewinder Type Providers
  • 29. © Mathias Brandewinder / @brandewinder No data, no learning »Most of ML effort is spent acquiring data »Most of the World is not in your Type System »Unpleasant trade-off: › Dynamic: easy hacking but runtime exceptions › Static: safer, but straight-jacket
  • 30. © Mathias Brandewinder / @brandewinder Demo »http://www.youtube.com/watch?v=cCuGgA9Yqrs
  • 31. © Mathias Brandewinder / @brandewinder Conclusion
  • 32. © Mathias Brandewinder / @brandewinder F# is a perfect fit for ML on .NET »Functional style fits very well with ML »REPL/interactive experience is crucial »Smooth integration with all of .NET »Flexible exploration, performance in production »Type Providers: static types, without the pain
  • 33. © Mathias Brandewinder / @brandewinder My recommendation »Take a look at Machine Learning, Data Science »Do it with a functional language »… and preferably, do it using F#
  • 34. © Mathias Brandewinder / @brandewinder Getting involved »Very dynamic community »FSharp.org, the F# Foundation »#fsharp on Twitter