1. Machine Learning
in ruby
Timothy N.Tsvetkov
2013

2. “Machine learning is a field of study that gives
computers the ability to learn without being explicitly
programmed”
Arthur Samuel, 1959

4. Types of algorithms
• supervised learning
• unsupervised learning (e.g. clustering)
• trunsduction
• reinforcement learning
• learning to learn

5. Recommendation
systems

6. SingularValue Decomposition
(SVD, LSI, projection)

7. Bob Mary John Abe
Doctor 6
Doctor 7
Doctor 8
Doctor 9
Doctor 10
Doctor 11
2 2 3 4
3 0 0 5
0 2 3 4
2 4 4 5
5 5 5 0
5 5 4 0

8. New user comes
D6 D7 D8 D9 D10 D11
Hero 2 0 0 2 5 5
We want to predict

10. Eckart andYoung theorem

11. https://github.com/
tukan/xxx/blob/master/
svd_detailed.rb

12. UU
0.351038 -0.284822
0.286974 -0.510018
0.286974 -0.357035
0.485518 -0.309601
0.521028 0.476186
0.484608 0.456813
SS
15.186791 0
0 7.341579
V.transposeV.transpose
0.486466 0.265078
0.542990 0.291882
0.553091 0.142232
0.403240 -0.907915
Decomposition

13. Plotting results

14. Plotting Hero

15. cosine-based similarity

16. Predicted results
Bob with similarity: 0.949
Mary with similarity: 0.947
Predicted:
Doctor 7: 3 (from Bob)
Doctor 8: 2 (from Mary)

17. Bob Mary John Abe Hero
Doctor 6
Doctor 7
Doctor 8
Doctor 9
Doctor 10
Doctor 11
2 2 3 4 2
3 0 0 5 3
0 2 3 4 2
2 4 4 5 2
5 5 5 0 5
5 5 4 0 5

18. Decision Trees
inductive machine learning

19. Censored

20. gem decisiontree

21. https://github.com/
tukan/xxx/blob/master/
decisiontree.rb

22. SupportVector
Machines
supervised machine learning

23. In a search of a perfect
hyperplane

25. Kernel-trick
Takes O(n^2) to compute
and only O(n) to compute Kernel

26. RBF Kernel