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Machine learning: How it can help your business...

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March 21, 2018
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Machine learning: How it can help your business - Microsoft Future Decoded - Budapest, March 2018

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szilard

March 21, 2018
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Transcript

  1. Machine Learning: How It Can Help Your Business Szilárd Pafka,

    PhD Chief Scientist, Epoch (USA) Microsoft Future Decoded, Budapest March 2018
  2. None

  3. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
  4. None

  5. Source: Andrew Ng

  6. None

  7. y = f(x) “Learn” f from data Source: Hastie etal,

    ESL 2ed

  8. Machine Learning linear/logistic regression decision trees neural networks support vector

    machines random forests gradient boosting deep learning neural networks

  9. Machine Learning linear/logistic regression (early 1900s/60s) decision trees (60s/80s) neural

    networks (60s/80s) support vector machines (90s) random forests (90s) gradient boosting (90s) deep learning neural networks (2000s)
  10. None

  11. data mining Source: Szilard Pafka

  12. data science Source: Szilard Pafka

  13. data science Source: Szilard Pafka

  14. CRISP-DM, 1999

  15. data $$$

  16. How?

  17. None
  18. None

  19. Source: Andrew Ng

  20. None
  21. None
  22. None

  23. Source: @iamdevloper (twitter)

  24. None
  25. None
  26. None

  27. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL

  28. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL better answer: it depends

  29. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all

  30. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all extra accuracy: combine them (ensembles)
  31. None
  32. None

  33. 10x

  34. None
  35. None
  36. None

  37. ML training: lots of CPU cores lots of RAM limited

    time
  38. None
  39. None
  40. None
  41. None
  42. None

  43. Source: Szilard Pafka

  44. None

  45. Random forest GBM GBM + cross validation GBM + hyperparameter

    tuning Logistic regression Neural Nets / Deep Learning Ensembles
  46. None
  47. None
  48. None
  49. None
  50. None
  51. None

  52. Backup Slides

  53. None

  54. 10x

  55. None
  56. None
  57. None

  58. Source: Szilard Pafka: 10 Pitfalls in Data Science, LA Data

    Science Meetup, February, 2014
  59. None
  60. None