Machine Learning on Azure - AzureConf
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Machine Learning can often be a daunting subject to tackle much less utilize in a meaningful manner. In this session, attendees will learn how to take their existing data, shape it, and create models that automatically can make principled business decisions directly in their applications. The discussion will include explanations of the data acquisition and shaping process. Additionally, attendees will learn the basics of machine learning - primarily the supervised learning problem.
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Machine Learning on Azure - AzureConf
- 1. Machine Learning on Azure Seth Juarez Analytics Program Manager DevExpress @sethjuarez
- 2. Questions? #azureconf on Twitter
- 3. Agenda 1) data science 2) prediction 3) process 4) models 5) AzureML
- 4. data science • key word: “science” • try stuff • it (might not | won’t) work the first time question • this might work… research • wikipedia time hypothesis • I have an idea experiment • try it out analysis • did this even work? conclusion • time for a better idea
- 5. machine learning • finding (and exploiting) patterns in data • replacing “human writing code” with “human supplying data” • system figures out what the person wants based on examples • need to abstract from “training” examples to “test” examples • most central issue in ML: generalization
- 6. machine learning • split into two (ish) areas • supervised learning • predicting the future • learn from past examples to predict future • unsupervised learning • understanding the past • making sense of data • learning structure of data • compressing data for consumption
- 9. neat applications • spam catchers • ocr (optical character recognition) • natural language processing • machine translation • biology • medicine • robotics (autonomous systems) • etc… 9
- 11. making decisions • what kinds of decisions are we making? • binary classification • yes/no, 1/0, male/female • multi-class classification • {A, B, C, D, F} (Grade), {1, 2, 3, 4} (Class), {teacher, student, secretary} • regression • number between 0 and 100, real value 11
- 12. process data clean transform maths predict model
- 13. data Class Outlook Temp. Windy Play Sunny Low Yes No Play Sunny High Yes No Play Sunny High No Play Overcast Low Yes Play Overcast High No Play Overcast Low No No Play Rainy Low Yes Play Rainy Low No ? Sunny Low No label (y) play / no play features outlook, temp, windy values (x) [Sunny, Low, Yes] Labeled dataset is a collection of (X, Y) pairs. Given a new x, how do we predict y?
- 14. clean / transform / maths Class Outlook Temp. Windy Play Sunny Lowest Yes No Play ? High Yes No Play Sunny High KindOf Play Overcast ? Yes Play Turtle Cloud High No Play Overcast ? No No Play Rainy Low 28% Play Rainy Low No ? Sunny Low No need to clean up data need to convert to model-able form (linear algebra) yak shaving Any apparently useless activity which, by allowing you to overcome intermediate difficulties, allows you to solve a larger problem. I was doing a bit of yak shaving this morning, and it looks like it might have paid off. http://en.wiktionary.org/wiki/yak_shaving
- 15. clean / transform / maths Class Outlook Temp. Windy Play Sunny Low Yes No Play Sunny High Yes No Play Sunny High No Play Overcast Low Yes Play Overcast High No Play Overcast Low No No Play Rainy Low Yes Play Rainy Low No ? Sunny Low No need to clean up data need to convert to model-able form (linear algebra)
- 16. model Class Outlook Temp. Windy Play Sunny Low Yes No Play Sunny High Yes No Play Sunny High No Play Overcast Low Yes Play Overcast High No Play Overcast Low No No Play Rainy Low Yes Play Rainy Low No ? Sunny Low No
- 17. predict Class Outlook Temp. Windy ? Sunny Low No PLAY!!!
- 18. models how do we build them?
- 19. linear classifiers • in order to classify things properly we need: • a way to mathematically represent examples • a way to separate classes (yes/no) • “decision boundary” • excel example • graph example 19 MODELS
- 20. linear classifiers • dot product of vectors • [ 3, 4 ] ● [ 1, 2 ] = (3 × 1) + (4 × 2) = 11 • a ● b = | a | × | b | cos θ • When does this equal 0? • why would this be useful? • decision boundary can be represented using a single vector 20 MODELS
- 21. perceptron …and other linear models
- 22. linear classifiers • Frank Rosenblatt, Cornell 1957 • let’s make a line (by using a single vector) • take the dot product between the line and the new point • > 0 belongs to class 1 • < 0 belongs to class 2 • == 0 flip a coin we don’t know • for each example, if we make a mistake, move the line 22 MODELS
- 24. perceptron
- 25. what if….
- 27. kernel methods 2푛 + 푛 2 = 2n + 푛 푛−1 2 features….
- 28. perceptron • minimize mistakes by moving w arg min (풘,풃) 1 2 풘 2 subject to: 푦푖 풘 ∙ 풙풊 − 푏 ≥ 1 REMINDER
- 29. perceptron • eventually this becomes an optimization problem 퐿 훼 = 푛 푖=1 훼푖 − 1 2 푖,푗 푇풙푗 훼푖훼푗 푦푖푦푗풙푖 subject to: 훼푖 ≥ 0, 푛 푖=1 훼푖푦푖 = 0 REMINDER
- 30. perceptron • eventually this becomes an optimization problem 퐿 훼 = 푛 푖=1 훼푖 − 1 2 푖,푗 푇풙푗 훼푖훼푗 푦푖푦푗풙푖 subject to: 훼푖 ≥ 0, 푛 푖=1 훼푖푦푖 = 0 REMINDER
- 31. perceptron • eventually this becomes an optimization problem 퐿 훼 = 푛 푖=1 훼푖 − 1 2 푖,푗 훼푖훼푗푦푖푦푗 푘 풙푖 , 풙푗 subject to: 훼푖 ≥ 0, 푛 푖=1 훼푖푦푖 = 0 REMINDER dot product
- 32. perceptron • Frank Rosenblatt, Cornell 1957 • let’s make a line (by using a single vector) • take the dot product between the line and the new point • > 0 belongs to class 1 • < 0 belongs to class 2 • == 0 flip a coin we don’t know • for each example, if we make a mistake, move the line 32 REMINDER
- 33. kernel (one weird trick….) MODELS • store dot product in a table 푇풙0 ⋯ 풙0 풙0 푇풙푗 ⋮ ⋱ ⋮ 풙푇푖 풙0 ⋯ 풙푖 푇풙푗 • call it the “kernel matrix” and “kernel trick” • project into any space and still learn a linear model
- 34. support vector machines MODELS • this method is the basis for SVM’s • returns a set of vectors (<< n) to make decision • essentially changed the space to make it separable
- 35. kernels • polynomial kernel 퐾 풙, 풚 = 풙푇풚 + 푐 푑 • RBF kernel 퐾 풙, 풚 = exp − 풙 − 풚 2 2 2휎2 MODELS 1
- 36. 36
- 37. what if….
- 39. neural networks
- 40. neural networks Play? ℎ1 ℎ2 ℎ3 퐵1
- 41. LINEAR METHODS
- 43. decision trees Class Outlook Temp. Windy Play Sunny Low Yes No Play Sunny High Yes No Play Sunny High No Play Overcast Low Yes Play Overcast High No Play Overcast Low No No Play Rainy Low Yes Play Rainy Low No ? Sunny Low No
- 44. decision trees • how should the computer split? • information gain (with entropy) • entropy measures how disorganized your answer is. • information gain says: • if I separate the answer by the values in a particular column, does the answer become *more* organized?
- 45. decision trees • calculating information gain: 퐼퐺 푦, 푎 = 퐻 푦 − 퐻 푦 푎) 푎 ∈ 퐴푡푡푟(푥) • 퐻 푦 – how messy is the answer • 퐻 푦 푎) – how messy is the answer if we know a?
- 47. POPULAR MODELS
- 48. do they work? testing
- 49. how well is it doing? Train Test Use 80% Use 20%
- 50. AzureML putting it all together 50
- 51. process reminder (same on Azure) data clean transform maths predict model
- 52. experiments putting it all together 52
- 53. Truth true false Guess positive 푡푟푢푒 푝표푠푖푡푖푣푒 푓푎푙푠푒 푝표푠푖푡푖푣푒 푝푟푒푐푖푠푖표푛 = 푡푝 푡푝 + 푓푝 negative 푓푎푙푠푒 푛푒푔푎푡푖푣푒 푡푟푢푒 푛푒푔푎푡푖푣푒 푟푒푐푎푙푙 = 푡푝 푡푝 + 푓푛 푎푐푐푢푟푎푐푦 = 푡푝 + 푡푛 푡푝 + 푡푛 + 푓푝 + 푓푛 confusion matrix
- 54. AzureML WebServices putting it all together 54
- 55. Get started with a free trial http://aka.ms/AzureConf2014 Or, use your existing benefits… http://aka.ms/AzureConf-MemberOffers
- 56. THANK YOU!!! AND STAY TUNED FOR THE NEXT SESSIONS!!!!!
- 57. Seth Juarez Analytics Program Manager, DevExpress @sethjuarez sethj@devexpress.com