CS 541: Artificial Intelligence

1. CS 541: Artificial Intelligence Lecture IX: Supervised Machine Learning • Slides Credit:Peter Norvig and Sebastian Thrun

2. Schedule • TA: Vishakha Sharma • Email Address: vsharma1@stevens.edu

3. Re-cap of Lecture VIII • Temporal models use state and sensor variables replicated over time • Markov assumptions and stationarity assumption, so we need • Transition model P(Xt|Xt-1) • Sensor model P(Et|Xt) • Tasks are filtering, prediction, smoothing, most likely sequence; • All done recursively with constant cost per time step • Hidden Markov models have a single discrete state variable; • Widely used for speech recognition • Kalman filters allow n state variables, linear Gaussian, O(n3) update • Dynamic Bayesian nets subsume HMMs, Kalman filters; exact update intractable • Particle filtering is a good approximate filtering algorithm for DBNs

4. Outline • Machine Learning • Classification (Naïve Bayes) • Regression (Linear, Smoothing) • Linear Separation (Perceptron, SVMs) • Non-parametric classification (KNN)

5. Machine Learning

6. Machine Learning • Up until now: how to reason in a give model • Machine learning: how to acquire a model on the basis of data / experience • Learning parameters (e.g. probabilities) • Learning structure (e.g. BN graphs) • Learning hidden concepts (e.g. clustering)

7. Machine Learning Lingo

8. Supervised Machine Learning Given a training set: (x1, y1), (x2, y2), (x3, y3), …(xn, yn) Where each yiwas generated by an unknown y = f (x), Discover a function h that approximates the true function f.

9. Classification (Naïve Bayes)

10. Classification Example: Spam Filter • Input: x = email • Output: y = “spam” or “ham” • Setup: • Get a large collection of example emails, each labeled “spam” or “ham” • Note: someone has to hand label all this data! • Want to learn to predict labels of new, future emails • Features: The attributes used to make the ham / spam decision • Words: FREE! • Text Patterns: $dd, CAPS • Non-text: SenderInContacts • … Dear Sir. First, I must solicit your confidence in this transaction, this is by virture of its nature as being utterly confidencial and top secret. … TO BE REMOVED FROM FUTURE MAILINGS, SIMPLY REPLY TO THIS MESSAGE AND PUT "REMOVE" IN THE SUBJECT. 99 MILLION EMAIL ADDRESSES FOR ONLY $99 Ok, Iknow this is blatantly OT but I'm beginning to go insane. Had an old Dell Dimension XPS sitting in the corner and decided to put it to use, I know it was working pre being stuck in the corner, but when I plugged it in, hit the power nothing happened.

11. A Spam Filter • Naïve Bayes spam filter • Data: • Collection of emails, labeled spam or ham • Note: someone has to hand label all this data! • Split into training, held-out, test sets • Classifiers • Learn on the training set • (Tune it on a held-out set) • Test it on new emails Dear Sir. First, I must solicit your confidence in this transaction, this is by virture of its nature as being utterly confidencial and top secret. … TO BE REMOVED FROM FUTURE MAILINGS, SIMPLY REPLY TO THIS MESSAGE AND PUT "REMOVE" IN THE SUBJECT. 99 MILLION EMAIL ADDRESSES FOR ONLY $99 Ok, Iknow this is blatantly OT but I'm beginning to go insane. Had an old Dell Dimension XPS sitting in the corner and decided to put it to use, I know it was working pre being stuck in the corner, but when I plugged it in, hit the power nothing happened.

12. Naïve Bayes for Text • Bag-of-Words Naïve Bayes: • Predict unknown class label (spam vs. ham) • Assume evidence features (e.g. the words) are independent • Generative model • Tied distributions and bag-of-words • Usually, each variable gets its own conditional probability distribution P(F|Y) • In a bag-of-words model • Each position is identically distributed • All positions share the same conditional probs P(W|C) • Why make this assumption? Word at position i, not ith word in the dictionary!

13. General Naïve Bayes • General probabilistic model: • General naive Bayes model: • We only specify how each feature depends on the class • Total number of parameters is linear in n |Y| x |F|nparameterss Y F1 F2 Fn n x |F| x |Y| parameters |Y| parameters

14. Example: Spam Filtering • Model: • What are the parameters? • Where do these tables come from? ham : 0.66 spam: 0.33 the : 0.0156 to : 0.0153 and : 0.0115 of : 0.0095 you : 0.0093 a : 0.0086 with: 0.0080 from: 0.0075 ... the : 0.0210 to : 0.0133 of : 0.0119 2002: 0.0110 with: 0.0108 from: 0.0107 and : 0.0105 a : 0.0100 ... Counts from examples!

15. Spam Example P(spam | words) = e-76 / (e-76 + e-80.5) = 98.9%

16. Example: Overfitting • Posteriors determined by relative probabilities (odds ratios): south-west : inf nation : inf morally : inf nicely : inf extent : inf seriously : inf ... screens : inf minute : inf guaranteed : inf $205.00 : inf delivery : inf signature : inf ... What went wrong here?

17. Generalization and Overfitting • Raw counts will overfit the training data! • Unlikely that every occurrence of “minute” is 100% spam • Unlikely that every occurrence of “seriously” is 100% ham • What about all the words that don’t occur in the training set at all? 0/0? • In general, we can’t go around giving unseen events zero probability • At the extreme, imagine using the entire email as the only feature • Would get the training data perfect (if deterministic labeling) • Wouldn’t generalize at all • Just making the bag-of-words assumption gives us some generalization, but isn’t enough • To generalize better: we need to smooth or regularize the estimates

18. Estimation: Smoothing • Maximum likelihood estimates: • Problems with maximum likelihood estimates: • If I flip a coin once, and it’s heads, what’s the estimate for P(heads)? • What if I flip 10 times with 8 heads? • What if I flip 10M times with 8M heads? • Basic idea: • We have some prior expectation about parameters (here, the probability of heads) • Given little evidence, we should skew towards our prior • Given a lot of evidence, we should listen to the data r g g

19. Estimation: Laplace Smoothing • Laplace’s estimate (extended): • Pretend you saw every outcome k extra times • What’s Laplace with k = 0? • k is the strength of the prior • Laplace for conditionals: • Smooth each condition independently: H H T

20. Estimation: Linear Interpolation • In practice, Laplace often performs poorly for P(X|Y): • When |X| is very large • When |Y| is very large • Another option: linear interpolation • Also get P(X) from the data • Make sure the estimate of P(X|Y) isn’t too different from P(X) • What if is 0? 1?

21. Real NB: Smoothing • For real classification problems, smoothing is critical • New odds ratios: helvetica : 11.4 seems : 10.8 group : 10.2 ago : 8.4 areas : 8.3 ... verdana : 28.8 Credit : 28.4 ORDER : 27.2 <FONT> : 26.9 money : 26.5 ... Do these make more sense?

22. Tuning on Held-Out Data • Now we’ve got two kinds of unknowns • Parameters: the probabilities P(Y|X), P(Y) • Hyperparameters, like the amount of smoothing to do: k • How to learn? • Learn parameters from training data • Must tune hyperparameters on different data • Why? • For each value of the hyperparameters, train and test on the held-out (validation)data • Choose the best value and do a final test on the test data

23. How to Learn • Data: labeled instances, e.g. emails marked spam/ham • Training set • Held out (validation) set • Test set • Features: attribute-value pairs which characterize each x • Experimentation cycle • Learn parameters (e.g. model probabilities) on training set • Tune hyperparameters on held-out set • Compute accuracy on test set • Very important: never “peek” at the test set! • Evaluation • Accuracy: fraction of instances predicted correctly • Overfitting and generalization • Want a classifier which does well on test data • Overfitting: fitting the training data very closely, but not generalizing well to test data Training Data Held-Out Data Test Data

24. What to Do About Errors? • Need more features– words aren’t enough! • Have you emailed the sender before? • Have 1K other people just gotten the same email? • Is the sending information consistent? • Is the email in ALL CAPS? • Do inline URLs point where they say they point? • Does the email address you by (your) name? • Can add these information sources as new variables in the Naïve Bayes model

25. A Digit Recognizer • Input: x = pixel grids • Output: y = a digit 0-9

26. Example: Digit Recognition • Input: x = images (pixel grids) • Output: y = a digit 0-9 • Setup: • Get a large collection of example images, each labeled with a digit • Note: someone has to hand label all this data! • Want to learn to predict labels of new, future digit images • Features: The attributes used to make the digit decision • Pixels: (6,8)=ON • Shape Patterns: NumComponents, AspectRatio, NumLoops • … 0 1 2 1 ??

27. Naïve Bayes for Digits • Simple version: • One feature Fij for each grid position <i,j> • Boolean features • Each input maps to a feature vector, e.g. • Here: lots of features, each is binary valued • Naïve Bayes model:

28. Learning Model Parameters

29. Problem: Overfitting 2 wins!!

30. Regression (Linear, smoothing)

31. Regression • Start with very simple example • Linear regression • What you learned in high school math • From a new perspective • Linear model • y = m x + b • hw(x) = y = w1x + w0 • Find best values for parameters • “maximize goodness of fit” • “maximize probability” or “minimize loss”

32. Regression: Minimizing Loss • Assume true function f is given byy = f (x) = m x + b + noisewhere noise is normally distributed • Then most probable values of parametersfound by minimizing squared-error loss: Loss(hw) = Σj(yj – hw(xj))2

33. Regression: Minimizing Loss

34. Regression: Minimizing Loss Choose weights to minimize sum of squared errors

35. Regression: Minimizing Loss

36. Regression: Minimizing Loss Linear algebra givesan exact solution to the minimization problem y = w1x + w0

37. Linear Algebra Solution

38. Don’t Always Trust Linear Models

39. Regression by Gradient Descent w = any point loop until convergence do: for each wi in w do: wi = wi – α ∂Loss(w) ∂ wi

40. Multivariate Regression • You learned this in math class too • hw(x) = w ∙ x = w xT = Σi wi xi • The most probable set of weights, w*(minimizing squared error): • w* = (XT X)-1XT y

41. Overfitting • To avoid overfitting, don’t just minimize loss • Maximize probability, including prior over w • Can be stated as minimization: • Cost(h) = EmpiricalLoss(h) + λ Complexity(h) • For linear models, consider • Complexity(hw) = Lq(w) = ∑i | wi |q • L1regularization minimizes sum of abs. values • L2regularization minimizes sum of squares

42. Regularization and Sparsity • Cost(h) = EmpiricalLoss(h) + λ Complexity(h) L1regularization L2regularization

43. Linear Separation Perceptron, SVM

44. Linear Separator -

45. Perceptron -

46. Perceptron Algorithm • Start with random w0, w1 • Pick training example <x,y> • Update (α is learning rate) • w1 w1+α(y-f(x))x • w0 w0+α(y-f(x)) • Converges to linear separator (if exists) • Picks “a” linear separator (a good one?)

47. What Linear Separator to Pick? -

48. What Linear Separator to Pick? Maximizes the “margin” - Support Vector Machines

49. Non-Separable Data? X2 - X1 - X3 Not linearly separable for x1, x2 What if we add a feature? x3= x12+x22 See: “Kernel Trick”

50. Non-parametric classification