This document summarizes Phillip Trelford's presentation on F# tools for machine learning and data science. It discusses F# meetup groups in London and Copenhagen, case studies using F# for modeling Halo 3 player skills and AdCenter learning algorithms, and live demos of F# for k-means clustering, type providers for CSV and R, and the FSLab library. It also provides testimonials on F# from Microsoft and Amyris and resources for learning more about F#.

1. F# IN YOUR PIPE
COPENHAGEN R
Phil Trelford
@ptrelford

2. F#UNCTIONAL LONDONERS
Founded Feb 2010
800 Members
Meet every 2 weeks
Key topics:
 Functional Programming
 Machine Learning
 Apps and Games
http://meetup.com/fsharplondon

3. CASE STUDIES Phillip Trelford, @ptrelford
Copenhagen R, 2014

4. F# TOOLS FOR HALO 3
Questions
Controllable player skill distribution (slow down!)
Controllable skills distributions (re-ordering)
Simulations
Large scale simulation of 8,000,000,000 matches
Distributed computation – 15 machines for 2wks
Tools
Result viewer (Logged results: 52GB of data)
Real-time simulator of partial update

5. ADCENTER
Weeks of data in training:
7,000,000,000 impressions
2 weeks of CPU time during
sessions
2 wks x 7 days x 86,400 sec/day
Learning algorithm speed
requirement:
5,787 impression updates /sec
172.8 µs per impression update

6. TESTIMONIALS – F# IN
MACHINE LEARNING
Phillip Trelford, @ptrelford
Copenhagen R, 2014

7. FSHARP.ORG/TESTIMONIALS -
MICROSOFT
For a machine learning scientist, speed of experimentation is the
critical factor to optimize.
Compiling is fast but loading large amounts of data in memory takes a
long time.
With F#’s REPL, you only need to load the data once
and you can then code and explore in the interactive environment.
Unlike C# and C++, F# was designed for this mode of interaction.
- Patrice Simard, Microsoft

8. FSHARP.ORG/TESTIMONIALS -
AMYRIS BIOTECH
F# has been phenomenally useful.
I would be writing a lot of this in Python otherwise
and F# is more robust, 20x - 100x faster to run
and for anything but the most trivial programs,
faster to develop.
- Darren Platt, Amyris Biotechnology

9. LIVE DEMOS Phil Trelford, @ptrelford
Copenhagen R, 2014

10. PIPE IT
load(transform(extract(data)))
f3( f2( f1( x ) ) )
let (|>) x f = f x
x |> f1 |> f2 |> f3
data |> extract |> transform |> load

11. K-MEANS CLUSTERING ALGORITHM
(* K-Means Algorithm *)
/// Group all the vectors by the nearest center.
let classify centroids vectors =
vectors |> Array.groupBy (fun v -> centroids |> Array.minBy (distance v))
/// Repeatedly classify the vectors, starting with the seed centroids
let computeCentroids seed vectors =
seed |> Seq.iterate (fun centers -> classify centers vectors
|> Array.map (snd >> average))

12. TYPE PROVIDERS
JSON
XML
CSV
Excel
SQL
R
MATLAB
Hadoop
…

13. CSV TYPE PROVIDER

14. R – TYPE PROVIDER

15. FSLAB
Data
Charting
Deedle
Math.Net
R Provider

16. RESOURCES Phil Trelford, @ptrelford
Copenhagen R, 2014

17. TRY IT BEFORE YOU BUY IT

18. BUY THE BOOK

19. JOIN THE COMMUNITY
F# Foundation
http://fsharp.org
Copenhagen F# Meetup
http://www.meetup.com/MoedegruppeFunktionelleKoebenhavnere/
Progressive F# Tutorials London
https://skillsmatter.com/conferences/1926-progressive-f-tutorials-
2014