6.S093 Visual Recognition through Machine Learning Competition

1. 6.S093 Visual Recognition through Machine Learning Competition Joseph Lim and Aditya Khosla Acknowledgment: Many slides from David Sontag and Machine Learning Group of UT Austin Image by kirkh.deviantart.com

2. What is Machine Learning???

3. What is Machine Learning??? According to the Wikipedia, Machine learning concerns the construction and study of systems that can learn from data.

4. What is ML? • Classification • From data to discrete classes

5. What is ML? • Classification • From data to discrete classes • Regression • From data to a continuous value

6. What is ML? • Classification • From data to discrete classes • Regression • From data to a continuous value • Ranking • Comparing items

7. What is ML? • Classification • From data to discrete classes • Regression • From data to a continuous value • Ranking • Comparing items • Clustering • Discovering structure in data

8. What is ML? • Classification • From data to discrete classes • Regression • From data to a continuous value • Ranking • Comparing items • Clustering • Discovering structure in data • Others

9. Classification:data to discrete classes • Spam filtering Spam Regular Urgent

10. Classification:data to discrete classes • Object classification Fries Hamburger None

11. Regression: data to a value • Stock market

12. Regression: data to a value • Weather prediction

13. Clustering: Discovering structure Set of Images

14. Clustering

15. Clustering • Unsupervised learning • Requires data, but NO LABELS • Detect patterns • Group emails or search results • Regions of images • Useful when don’t know what you’re looking for

16. Clustering • Basic idea: group together similar instances • Example: 2D point patterns

17. Clustering • Basic idea: group together similar instances • Example: 2D point patterns

18. Clustering • Basic idea: group together similar instances • Example: 2D point patterns

19. Clustering • Basic idea: group together similar instances • Example: 2D point patterns • What could “similar” mean? • One option: small Euclidean distance • Clustering is crucially dependent on the measure of similarity (or distance) between “points”

20. K-Means • An iterative clustering algorithm • Initialize: Pick K random points as cluster centers • Alternate: • Assign data points to closest cluster center • Change the cluster center to the average of its assigned points • Stop when no points’ assignments change Animation is from Andrey A. Shabalin’s website

21. K-Means • An iterative clustering algorithm • Initialize: Pick K random points as cluster centers • Alternate: • Assign data points to closest cluster center • Change the cluster center to the average of its assigned points • Stop when no points’ assignments change

22. Properties of K-means algorithm • Guaranteed to converge in a finite number of iterations • Running time per iteration: • Assign data points to closest cluster center O(KN) • Change the cluster center to the average of its assigned points O(N)

23. Example: K-Means for Segmentation K=2 Original Goal of Segmentation is to partition an image into regions each of which has reasonably homogenous visual appearance.

24. Example: K-Means for Segmentation K=2 K=10 K=3 Original

25. Pitfalls of K-Means • K-means algorithm is heuristic • Requires initial means • It does matter what you pick! • K-means can get stuck K=1 should be better K=2 should be better

26. Classification

27. Typical Recognition System +1 bus classify features -1 not bus x F(x) y • Extract features from an image • A classifier will make a decision based on extracted features

28. Typical Recognition System +1 bus classify features -1 not bus x F(x) y • Extract features from an image • A classifier will make a decision based on extracted features

29. Classification • Supervised learning • Requires data, AND LABELS • Useful when you know what you’re looking for

30. Linear classifier • Which of these linear separators is optimal?

31. Maximum Margin Classification • Maximizing the margin is good according to intuition and PAC theory. • Implies that only support vectors matter; other training examples are ignorable.

32. Classification Margin • Distance from example xi to the separator is • Examples closest to the hyperplane are support vectors. • Marginρ of the separator is the distance between support vectors. ρ r

33. Linear SVM Mathematically • Let training set {(xi, yi)}i=1..n, xi  Rd, yi{-1, 1}be separated by a hyperplane withmargin ρ. Then for each training example (xi, yi): • For every support vector xs the above inequality is an equality. After rescaling w and b by ρ/2in the equality, we obtain that distance between each xs and the hyperplane is • Then the margin can be expressed through (rescaled) w and b as: wTxi+ b ≤ - ρ/2 if yi= -1 wTxi+ b ≥ ρ/2 if yi= 1 yi(wTxi+ b) ≥ ρ/2 

34. Linear SVMs Mathematically (cont.) • Then we can formulate the quadratic optimization problem: Find w and b such that is maximized and for all (xi, yi), i=1..n : yi(wTxi+ b) ≥ 1 Find w and b such that Φ(w) = ||w||2=wTw is minimized and for all (xi, yi), i=1..n : yi (wTxi+ b) ≥ 1

35. Is this perfect?

36. Is this perfect?

37. Soft Margin Classification • What if the training set is not linearly separable? • Slack variablesξi can be added to allow misclassification of difficult or noisy examples, resulting margin called soft. ξi ξi

38. Soft Margin Classification Mathematically • The old formulation: • Modified formulation incorporates slack variables: • Parameter C can be viewed as a way to control overfitting: it “trades off” the relative importance of maximizing the margin and fitting the training data. Find w and b such that Φ(w) =wTw is minimized and for all (xi,yi),i=1..n: yi (wTxi+ b) ≥ 1 Find w and b such that Φ(w) =wTw + CΣξi is minimized and for all (xi,yi),i=1..n: yi (wTxi+ b) ≥ 1 – ξi, , ξi≥ 0

39. Exercise • Exercise 1. Implement K-means • Exercise 2. Play with SVM’s C-parameter