1.Machine Learning in ruby Timothy N.Tsvetkov 2013

2. Machine learning is a eld of study that gives computers the ability to learn without being explicitly programmed Arthur Samuel, 1959 3. Types of algorithms supervised learning unsupervised learning (e.g. clustering) trunsduction reinforcement learning learning to learn 4. Recommendation systems 5. SingularValue Decomposition (SVD, LSI, projection) 6. 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 7. New user comes D6 D7 D8 D9 D10 D11 Hero 2 0 0 2 5 5 We want to predict 8. Eckart andYoung theorem 9. https://github.com/ tukan/xxx/blob/master/ svd_detailed.rb 10. 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 11. Plotting results 12. Plotting Hero 13. cosine-based similarity 14. Predicted results Bob with similarity: 0.949 Mary with similarity: 0.947 Predicted: Doctor 7: 3 (from Bob) Doctor 8: 2 (from Mary) 15. 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 16. Decision Trees inductive machine learning 17. Censored 18. gem decisiontree 19. https://github.com/ tukan/xxx/blob/master/ decisiontree.rb 20. SupportVector Machines supervised machine learning 21. In a search of a perfect hyperplane 22. Kernel-trick Takes O(n^2) to compute and only O(n) to compute Kernel 23. RBF Kernel