Machine Learning on.NET F# FTW!

A few words about me  Mathias Brandewinder  Background: economics, operations research .NET developer for 10~ years (C#, F#)  Bay.Net San Francisco, SFSharp.org  Yes I have an accent 

I am assuming…  Few familiar with F#  Mostly unfamiliar with Data Science / Machine Learning  Mostly familiar with C#, VB.NET  Some familiar with Functional Languages

Why this talk  Machine Learning, Data Science are red-hot topics ›... and relevant to developers .NET is under-represented

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

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]

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

The plan  Classification  Regression  Unsupervised  Type Providers  Existing.NET libraries  Algebra  Functional fit

Classification & Regression

Goal  What does “a day of Machine Learning” look like?  Illustrate Classification and Regression

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

Support Vector Machine  Classic algorithm  Tries to separate the 2 classes by the widest possible margin  Using Accord.NET implementation

Demo

Take-aways  F# is a first-class citizen in.NET  Decent libraries: Accord.NET, Math.NET, Alea.cuBase, …  Interactive experience with the REPL  Syntax matters!  Classification, Regression, Cross-Validation

Unsupervised

Goal  Illustrate unsupervised learning  Functional programming and ML are a great fit

Writing your own  Usually not advised  Useful for ML because ›You don’t always have a library ›As you learn your domain, you may need a custom model

Most ML algorithms are the same  Read data  Transform into Features  Learn a Model from the Features  Evaluate Model quality

Translates well to FP  Read data  Transform into Features -> Map  Learn a Model from the Features -> Recursion  Evaluate Model quality -> Fold/Reduce

Focus on transforms, not objects  Need to transform rapidly Features ›Don’t force your domain to fit my algorithm ›Morph around the shape of the data, pass functions ›Algorithms need to be generic  FP is fantastic for code reuse

What is Unsupervised Learning?  “Tell me something about my data”  Example: Clustering ›Find groups of “similar” entities in my dataset

Example: clustering (1)

Example: clustering (2) “Assign to closest Centroid” [Distance]

Example: clustering (3) “Update Centroids based on Cluster” [Reduce]

Example: clustering (4) “Stop when no change” [Recursion]

Demo

Type Providers

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

Demo

Conclusion

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  Type Providers: static types, without the pain

My recommendation  Take a look at Machine Learning, Data Science  Do it with a functional language  … and preferably, do it using F#  Workshop this Friday!

Getting involved  Very dynamic community  FSharp.org, the F# Foundation  Machine Learning working group, FsLab.org  Kaggle.com

Contacting me 