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Mathias Brandewinder / @brandewinder Machine Learning on .NET F# FTW!

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Page 1: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Machine Learning on .NET

F# FTW!

Page 2: Machine learning-with-f sharp

© 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

http://www.clear-lines.com/blog
Page 3: Machine learning-with-f sharp

© 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

Page 4: Machine learning-with-f sharp

© 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!

Page 5: Machine learning-with-f sharp

© 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

Page 6: Machine learning-with-f sharp

© 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)

http://www.github.com/fsharp
Page 7: Machine learning-with-f sharp

© 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]

Page 8: Machine learning-with-f sharp

© 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

Page 9: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

The plan

»Classification »Regression»Unsupervised

»Type Providers

»Existing .NET libraries

»Algebra»Functional fit

Page 10: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Classification & Regression

Page 11: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Goal

»What does “a day of Machine Learning”

look like?

»Illustrate Classification and Regression

Page 12: Machine learning-with-f sharp

© 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

Page 13: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Support Vector Machine

»Classic algorithm»Tries to separate the 2 classes by the widest possible margin

»Using Accord.NET implementation

Page 14: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Demo: Kaggle Digit Recognizer

Page 15: Machine learning-with-f sharp

© 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

Page 16: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Unsupervised

Page 17: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Goal

»Illustrate unsupervised learning

»Functional programming and ML are a

great fit

Page 18: Machine learning-with-f sharp

© 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

Page 19: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Most ML algorithms are the same»Read data

»Transform into Features

»Learn a Model from the Features

»Evaluate Model quality

Page 20: Machine learning-with-f sharp

© 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

Page 21: Machine learning-with-f sharp

© 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

Page 22: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

What is Unsupervised Learning?»“Tell me something about my data”

»Example: Clustering

› Find groups of “similar” entities in my dataset

Page 23: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Example: clustering (1)

Page 24: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Example: clustering (2)

“Assign to closest Centroid”[Map Distance]

Page 25: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Example: clustering (3)

“Update Centroids based on Cluster”[Reduce]

Page 26: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Example: clustering (4)

“Stop when no change”[Recursion]

Page 27: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Demo

Page 28: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Type Providers

Page 29: Machine learning-with-f sharp

© 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

Page 30: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Demo

»http://

www.youtube.com/watch?v=cCuGgA9Yqrs

http://www.youtube.com/watch?v=cCuGgA9Yqrs
http://www.youtube.com/watch?v=cCuGgA9Yqrs
Page 31: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Conclusion

Page 32: Machine learning-with-f sharp

© 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

Page 33: Machine learning-with-f sharp

© 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#

Page 34: Machine learning-with-f sharp

© Mathias Brandewinder / @brandewinder

Getting involved

»Very dynamic community

»FSharp.org, the F# Foundation

»#fsharp on Twitter


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