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Machine Learning What, how, why? Rmi Emonet (@remiemonet) - PowerPoint PPT Presentation

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Machine Learning What, how, why? Rmi Emonet (@remiemonet) 2015-09-30 Web En Vert $ whoami $ whoami Software Engineer Researcher: machine learning, computer vision Teacher: web technologies, computing literacy Geek: deck.js slides,

  1. Machine Learning What, how, why? Rémi Emonet (@remiemonet) 2015-09-30 Web En Vert

  2. $ whoami

  3. $ whoami Software Engineer Researcher: machine learning, computer vision Teacher: web technologies, computing literacy Geek: deck.js slides, isochrones, … You are shrewd, skeptical and restrained. You are independent: you have a strong desire to have time to yourself. You are calm-seeking: you prefer activities that are quiet, calm, and safe. And you are philosophical: you are open to and intrigued by new ideas and love to explore them. Experiences that give a sense of prestige hold some appeal to you. You are relatively unconcerned with both tradition and taking pleasure in life. You care more about making your own path than following what others have done. And you prefer activities with a purpose greater than just personal enjoyment. IBM BlueMix (Watson)

  4. What is Machine Learning? 4 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  5. Machine Learning Basic Principle Given a dataset { y , x , … , x } n i 1 ip i =1 i Optimize the likelihood function ∑ ∑ ∑ ∑ ∑ L = n ( w , t , d ) log p ( w , t ∣ z ) p ( z , t ∣ d ) a r s w t a z t s d Or using a sparse regularization ∑ ∑ L − λ KL ( U ∣∣ p ( ts ∣ z , d )) sparse z d By using a Gibbs Sampler − ji ( W , rt , z ) + η ( W , rt ) N − ji ji ji ji ji ji p ( W , at ∣ o = o , O ) = obs ′ ) ji ji ji ′ ( − ji ( w , rt , z ) + η ( w , rt ) ′ ′ ′ ∑ w , rt N ′ ji obs 5 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  6. Machine Learning in the Wild 6 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  7. Which One of These Services Uses Machine Learning? 7 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  8. Machine Learning in Future Tech? 8 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  9. What is Machine Learning? an example motivation 9 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  10. Challenge: Which Iris Species?

  11. Feature Extraction Sepal length: 5.1 ⇒ Sepal width: 2.5 Petal length: 4.2 Petal width: 1.0 Expected Label: “Iris Setosa” 11 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  12. Analysis and Program Writing

  13. IFTTT...

  14. Predictive Machine Learning Instead of writing a program that solves a task, We 1. collect labeled data: input/output pairs 2. automatically generate a program that computes an output for each new input 3. profit! The machine learns to generalize from a limited number of examples, like humans do. 14 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  15. Different Types of Tasks Supervised learning: some labels are known classification: find the label of an example regression: find the target value Unsupervised learning: no labels clustering: group things together pattern mining: find recurrent events anomaly detection: find “outliers” 15 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  16. The Principle of “Overfitting” 16 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  17. A Lot of Different Methods Also called “models” linear regression, logistic regression, SVM, kernel SVM, neural networks, k-means clustering, collaborative filtering, bayesian networks, expectation maximization, belief propagation, multiple kernel learning, metric learning, transfer learning, decision trees, gaussian processes, random forests, boosting, ... For different contexts different tasks different nature of data different suppositions on the data different amount of data 17 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  18. Different Ways to Start Use a product that uses ML e.g. adwords, ibm bluemix, … Use a prediction API send your data to the service get API to process new inputs e.g., google pred. API, prediction.io, ... Dive into machine learning… 18 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  19. Into Machine Learning Using libraries libraries exist in most languages most models already implemented test different methods with different parameters Learning machine learning many online courses get deeper understanding 19 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  20. Does Machine Learning Actually Matter? 20 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  21. Example: The Netflix Challenge 21 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  22. FAIR 22 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  23. Example: Facebook AI Research Director: Yann LeCun Scientific Leads Léon Bouttou Rob Fergus Florent Perronnin fr rest 23 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  24. Data, Data, Data 24 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  25. Data is Machine Learning's Fuel 25 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  26. data === power

  27. Getting Data?

  28. Getting Data? Collect from your services/applications Do it yourself Pay some people you know Use crowd-sourcing, e.g., Amazon Mechanical Turk (MTurk) Find existing datasets (open data, etc) Work for/with a “data rich” company Create your “intermediation” business 28 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  29. What Can It Do For Me 29 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  30. Search Google Search, Bing, etc 30 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  31. Advertising AdWords, etc 31 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  32. Recommendations Netflix, Amazon, Youtube, app Stores, etc 32 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  33. Text Translation 33 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  34. Optical Character Recognition (postcodes, checks, book scans, etc) 34 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  35. Visual Recognition (objects, plants, animals, etc) 35 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  36. Face Detection Smile Detection (embedded in Cameras) 36 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  37. Face Identification (Picasa, Facebook, etc) 37 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  38. Kinect Controller 38 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  39. Self Driving Cars 39 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  40. Voice Recognition and Synthesis (GoogleNow, Siri, Cortana) 40 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  41. Sound Recognition (birds, underwater sounds, safety, etc) 41 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  42. Fraud Detection (Banking, Websites, etc) 42 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  43. Automated Trading … 43 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  44. Customer/Person Profiling BlueMix Watson, etc 44 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  45. Adaptive Websites (automated A/B testing) 45 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  46. The “Big Data” Hype 46 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  47. and much more...

  48. Where Will it Stop? 48 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

  49. Singularity?

  50. Thanks! Questions? twitter: @remiemonet web/email: http://home.heeere.com Recommended Links: comprehensive introduction to ML models scikit learn (python) ... 50 / 50 − Rémi Emonet (@remiemonet) − Machine Learning What, how, why?

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