Intro slides for an simple Machine Learning Dojo with F#; the companion code is at bit.ly/FSharp-ML-Dojo

1. Clear Lines Consulting · clear-lines.com 5/20/2013 · 1
F# Coding Dojo
A gentle introduction to Machine
Learning with F#

2. Clear Lines Consulting · clear-lines.com 5/20/2013 · 2
The goal tonight
» Take a Kaggle data science contest
» Write some code and have fun
» Write a classifier, from scratch, using F#
» Learn some Machine Learning concepts
» Stretch goal: send results to Kaggle

3. Clear Lines Consulting · clear-lines.com 5/20/2013 · 3
What you may need to know

4. Clear Lines Consulting · clear-lines.com 5/20/2013 · 4
Kaggle Digit Recognizer contest
» Full description on Kaggle.com
» Dataset: hand-written digits (0, 1, … , 9)
» Goal = automatically recognize digits
» Training sample = 50,000 examples
» Contest: predict 20,000 “unknown” digits

5. Clear Lines Consulting · clear-lines.com 5/20/2013 · 5
The data “looks like that”
1

6. Clear Lines Consulting · clear-lines.com 5/20/2013 · 6
Real data
» 28 x 28 pixels
» Grayscale: each pixel 0 (white) to 255 (black)
» Flattened: one record = Number + 784 Pixels
» CSV file

7. Clear Lines Consulting · clear-lines.com 5/20/2013 · 7
Illustration (simplified data)
Pixels (real: 784 fields, from 0 to 255)Actual Number
1,0,0,255,0,0,255,255,0,0,0,255,0,0,0,255,0

8. Clear Lines Consulting · clear-lines.com 5/20/2013 · 8
What’s a Classifier?
» “Give me an unknown data point, and I will
predict what class it belongs to”
» In this case, classes = 0, 1, 2, … 9
» Unknown data point = scanned digit, without
the class it belongs to

9. Clear Lines Consulting · clear-lines.com 5/20/2013 · 9
The KNN Classifier
» KNN = K-Nearest-Neighbors algorithm
» Given an unknown subject to classify,
» Look up all the known examples,
» Find the K closest examples,
» Take a majority vote,
» Predict what the majority says

10. Clear Lines Consulting · clear-lines.com 5/20/2013 · 10
Illustration: 1 nearest neighbor
1
0
?
Sample Unknown
Which item from the sample
is nearest / closest to the Unknown
item we want to predict?
Suppose we have just 2 examples in the sample,
and want to predict the class of Unknown

11. Clear Lines Consulting · clear-lines.com 5/20/2013 · 11
What does “close” mean?
» To define “close” we need a distance
» We can use the distance between images as a
measure for “close”
» Other distances can be used as well
» Note: Square root not important here

12. Clear Lines Consulting · clear-lines.com 5/20/2013 · 12
Illustration: 1 nearest neighbor
1
0
?
Sample Unknown
X
1
X
X
X
X
X
X
X
X
0
Differences
Let’s compute the distance
between Unknown and our
two examples…

13. Clear Lines Consulting · clear-lines.com 5/20/2013 · 13
Illustration: 1 nearest neighbor
1
0
?
Sample
Unknown
1
0
?

    
(255-0)2
(255-0)2
(255-0)2 (0-255)2 Etc… Distance = 721
Distance = 255

14. Clear Lines Consulting · clear-lines.com 5/20/2013 · 14
Illustration: 1 nearest neighbor
1
0
?
SampleUnknown The first example is closest
to our Unknown candidate:
we predict that Unknown
has the same Number, 1

15. Clear Lines Consulting · clear-lines.com 5/20/2013 · 15
Questions?

16. Clear Lines Consulting · clear-lines.com 5/20/2013 · 16
Let’s start coding!
» Code 1-nearest-neighbor classifier
» “Guided script” available at:
» Bit.ly/FSharp-ML-Dojo
» https://gist.github.com/mathias-
brandewinder/5558573