Understanding Basics of Machine Learning
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This session covers basics of machine learning and mapping it to real-world scenarios.
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Understanding Basics of Machine Learning
- 1. Understanding basics of Machine Learning Pranav Ainavolu Microsoft MVP | Senior Developer at Realpage @a_pranav | http://pranavon.net/
- 2. Agenda 1) data science 2) prediction 3) process 4) models 5) AzureML
- 3. data science • key word: “science” • try stuff • it (might not | won’t) work the first time • this might work…question • wikipedia timeresearch • I have an ideahypothesis • try it outexperiment • did this even work?analysis • time for a better ideaconclusion
- 4. 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
- 5. 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
- 7. neat applications • spam catchers • ocr (optical character recognition) • natural language processing • machine translation • biology • medicine • robotics (autonomous systems) • etc… 7
- 9. 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 9
- 11. 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?
- 12. 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
- 13. 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)
- 14. 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
- 15. predict PLAY!!! Class Outlook Temp. Windy ? Sunny Low No
- 16. models how do we build them?
- 17. 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 17 MODELS
- 18. 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 18 MODELS
- 19. perceptron …and other linear models
- 20. 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 20 MODELS
- 22. perceptron
- 23. what if….
- 25. kernel methods 2𝑛 + 𝑛 2 = 2n + 𝑛 𝑛−1 2 features….
- 26. perceptron •minimize mistakes by moving w arg min (𝒘,𝒃) 1 2 𝒘 2 subject to: 𝑦𝑖 𝒘 ∙ 𝒙𝒊 − 𝑏 ≥ 1 REMINDER
- 27. perceptron •eventually this becomes an optimization problem 𝐿 𝛼 = 𝑖=1 𝑛 𝛼𝑖 − 1 2 𝑖,𝑗 𝛼𝑖 𝛼𝑗 𝑦𝑖 𝑦𝑗 𝒙𝑖 𝑇 𝒙𝑗 subject to: 𝛼𝑖 ≥ 0, 𝑖=1 𝑛 𝛼𝑖 𝑦𝑖 = 0 REMINDER
- 28. perceptron •eventually this becomes an optimization problem 𝐿 𝛼 = 𝑖=1 𝑛 𝛼𝑖 − 1 2 𝑖,𝑗 𝛼𝑖 𝛼𝑗 𝑦𝑖 𝑦𝑗 𝒙𝑖 𝑇 𝒙𝑗 subject to: 𝛼𝑖 ≥ 0, 𝑖=1 𝑛 𝛼𝑖 𝑦𝑖 = 0 REMINDER
- 29. perceptron •eventually this becomes an optimization problem 𝐿 𝛼 = 𝑖=1 𝑛 𝛼𝑖 − 1 2 𝑖,𝑗 𝛼𝑖 𝛼𝑗 𝑦𝑖 𝑦𝑗 𝑘 𝒙𝑖, 𝒙𝑗 subject to: 𝛼𝑖 ≥ 0, 𝑖=1 𝑛 𝛼𝑖 𝑦𝑖 = 0 REMINDER dot product
- 30. 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 30 REMINDER
- 31. kernel (one weird trick….) •store dot product in a table 𝒙0 𝑇 𝒙0 ⋯ 𝒙0 𝑇 𝒙𝑗 ⋮ ⋱ ⋮ 𝒙𝑖 𝑇 𝒙0 ⋯ 𝒙𝑖 𝑇 𝒙𝑗 •call it the “kernel matrix” and “kernel trick” •project into any space and still learn a linear model MODELS
- 32. support vector machines •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 MODELS
- 33. kernels •polynomial kernel 𝐾 𝒙, 𝒚 = 𝒙 𝑇 𝒚 + 𝑐 𝑑 •RBF kernel 𝐾 𝒙, 𝒚 = exp − 𝒙 − 𝒚 2 2 2𝜎2 MODELS 1
- 34. 34
- 35. what if….
- 37. neural networks
- 39. LINEAR METHODS
- 41. 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
- 42. 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?
- 43. decision trees •calculating information gain: • 𝐻 𝑦 – how messy is the answer • 𝐻 𝑦 𝑎) – how messy is the answer if we know a? 𝐼𝐺 𝑦, 𝑎 = 𝐻 𝑦 − 𝐻 𝑦 𝑎) 𝑎 ∈ 𝐴𝑡𝑡𝑟(𝑥)
- 45. POPULAR MODELS
- 47. how well is it doing? Train Test Use 80% Use 20%
- 48. AzureML putting it all together 48
- 49. process reminder (same on Azure) data clean transform maths model predict
- 50. experiments putting it all together 50
- 51. Truth true false Guess positive 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑝 negative 𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑛 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑡𝑝 + 𝑡𝑛 𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛 confusion matrix
- 52. Thank you!