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- 1. Machine Learning
- 2. Machine Learning A n Intro duction
- 3. Automated Insights
- 4. Spam
- 5. You might like ...
- 6. The World
- 7. People you should follow...
- 8. People you may know...
- 9. People you may know...
- 10. Classifying ClusteringRecommending
- 11. Classifying
- 12. Clustering
- 13. Recommending
- 14. ItemsUsers
- 15. ItemsUsers
- 16. ItemsUsers
- 17. ItemsUsers
- 18. ItemsUsers
- 19. Modeling
- 20. Similarity
- 21. Movies
- 22. Collaborative
- 23. How to represent our data?
- 24. Data User A User B User CItem 1 1.0 3.0 5.0
- 25. Similarity? User A User B User CItem 1 1.0 3.0 5.0Item 2 2.0 5.0 2.0Item 3 1.0 3.0 1.0
- 26. Euclidean Distance
- 27. Euclidean Distance q 1.0 2.0 1.0 p 2.0 5.0 3.0
- 28. Euclidean Distance User A User B User C dItem 1 1.0 3.0 5.0 4Item 2 2.0 5.0 2.0 2.45Item 3 1.0 3.0 1.0
- 29. Euclidean Distance(defn euclidean-distance [v m] (let [num-of-rows (ﬁrst (dim m)) difference (minus (matrix (repeat num-of-rows v)) m)] (sqrt (map sum-of-squares difference)))) Clojure #ftw
- 30. Content Based
- 31. Distance User A User B User CItem 1 1.0 3.0 5.0Item 2 2.0 5.0 2.0Item 3 1.0 3.0 1.0
- 32. Distance Feature A Feature B Feature CItem 1 1.0 3.0 5.0Item 2 2.0 5.0 2.0Item 3 1.0 3.0 1.0
- 33. Classification Algorithm
- 34. k-nearest neighbours
- 35. Our Data A B C dItem 1 1.0 3.0 5.0 4Item 2 2.0 5.0 2.0 2.45Item 3 1.0 3.0 1.0
- 36. Our Model A B C d Label {Trained Item 1 1.0 3.0 5.0 4 Spam Item 2 2.0 5.0 2.0 2.45 Ham Item 3 1.0 3.0 1.0
- 37. Our Model Label d {Trained Item 1 Spam 4 Item 2 Ham 2.45 Item 3
- 38. k-nn Classifier(defn knn-classify [xs k m labels] (let [sorted-labels (take k (map (partial nth labels) (sorted-indexes (euclidean-distance xs m)))) category (mode sorted-labels)] (if (seq? category) (ﬁrst category) category))) Clojure #ftw
- 39. Evaluation
- 40. Our Model Label d {Trained Item 1 Spam 4 Item 2 Ham 2.45 Item 3
- 41. Our Model Observed Label Calculated Label {Trained Item 1 Spam Item 2 HamTest Item 3 Ham Ham
- 42. kʼthx

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