Introduction to Machine Learning (UX Indonesia Meetup)

Introduction to Machine Learning (UX Indonesia Meetup)

For UX ID Meetup

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Galuh Sahid

June 23, 2020
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  1. Introduction to Machine Learning Galuh Sahid @galuhsahid | galuh.me UX

    Indonesia Meetup - June 23, 2020
  2. • Data Scientist at Gojek • Google Developer Expert in

    Machine Learning • Co-host podcast Kartini Teknologi (kartiniteknologi.id) @galuhsahid Hi! I’m Galuh.
  3. @galuhsahid Photo by Bram Van Oost from Unsplash

  4. @galuhsahid Photo by Frank V. from Unsplash

  5. @galuhsahid Photo by Bence Boros from Unsplash

  6. @galuhsahid Photo by Nordwood from Unsplash

  7. @galuhsahid Photo by Krsto Jevtic from Unsplash

  8. It’s an exciting time to learn about machine learning! @galuhsahid

  9. But… what is machine learning? @galuhsahid

  10. A field of study that gives computers the ability to

    learn without being explicitly programmed. Arthur Samuel (1959) @galuhsahid
  11. How is machine learning different from traditional programming? @galuhsahid

  12. Traditional Programming Rules Data Answers @galuhsahid

  13. if pixel[5][7] is black and pixel [5][6] is black and

    pixel [5][8] is black and …: if pixel[6][7] is black and pixel[6][7] is black and …: return “panda” … … … else: return “cat” Photo by Damian Patowski from Unsplash @galuhsahid
  14. if pixel[5][7] is black and pixel [5][6] is black and

    pixel [5][7] is black and …: if pixel[6][7] is black and pixel[6][7] is black and …: return “panda” … … … else: return “not cat” Photo by Dušan Smetana from Unsplash @galuhsahid
  15. Machine Learning Answers Data Rules

  16. Answers Data Panda Cat Cat Photo by Max Baskalov and

    Zane Lee from Unsplash Panda @galuhsahid
  17. ? Cat ? Photo by Cyrus Chew from Unsplash @galuhsahid

  18. Machine learning? Artificial intelligence? Neural networks? Deep learning? @galuhsahid

  19. Source @galuhsahid

  20. Rule engine Knowledge Graphs Source @galuhsahid

  21. Regression Decision Tree Random Forest Source @galuhsahid

  22. Ingredients of machine learning @galuhsahid

  23. Ingredient #1 Data @galuhsahid

  24. Tabular @galuhsahid

  25. Text @galuhsahid

  26. Sound @galuhsahid

  27. Image @galuhsahid

  28. Ingredient #2 Features @galuhsahid

  29. Examples of features for house price prediction Features @galuhsahid

  30. Examples of features for house price prediction Features We want

    to predict this… @galuhsahid
  31. Examples of features for house price prediction Features We want

    to predict this… …using these features @galuhsahid
  32. What are the features for an image classification task? Features

    @galuhsahid
  33. What are the features for an image classification task? Features

    @galuhsahid Source
  34. Ingredient #3 Model @galuhsahid

  35. What is a “model”? Model - A model maps examples

    to predicted labels - It is defined by weights that are learned during the training process - Once trained, you can use it to make predictions about data that it has never seen before @galuhsahid
  36. The training process Model - Iteration 1: 2*number of floors

    + 3*area size = predicted house price Model Data Predictions House #1: predicted: 200 million actual: 500 million difference: 300 million @galuhsahid
  37. The training process Model - Iteration 1: 2*number of floors

    + 3*area size = predicted house price Model Data Predictions House #1: predicted: 400 million actual: 500 million difference: 100 million - Iteration 2: 4*number of floors + 6*area size = predicted house price @galuhsahid
  38. The training process Model - Iteration 1: 2*number of floors

    + 3*area size = predicted house price Model Data Predictions House #1: predicted: 400 million actual: 500 million difference: 100 million - Iteration 2: 4*number of floors + 6*area size = predicted house price Our model does not get smart right away - it needs to be “trained” @galuhsahid
  39. Types of machine learning problems & applications @galuhsahid

  40. Type #1 Binary classification Classifies input into one of two

    categories @galuhsahid
  41. Spam or not spam Binary Classification @galuhsahid

  42. Type #2 Multi-class classification Classifies input into one of more

    than two categories @galuhsahid
  43. Language prediction Multi-class Classification @galuhsahid

  44. Type #3 Regression Predicts a value on a continuous scale

    @galuhsahid
  45. Visit duration & wait time estimates Regression @galuhsahid

  46. Type #4 Catalog organization Produces a set of result to

    present to users @galuhsahid
  47. Recommender System Catalog Organization @galuhsahid

  48. Type #5 Generative model Focuses on generating data rather than

    classifying or organizing it @galuhsahid
  49. Generating a new face Generative Model @galuhsahid

  50. Generative Model Generating a new text @galuhsahid talktotransformer.com

  51. Machine learning myths @galuhsahid

  52. Myth #1 Machine learning is neutral @galuhsahid

  53. Myth #1 Machine learning is neutral Machine learning systems are

    formulated and built by humans. Machine learning algorithms are trained by humans on data that are collected by humans and represent our (biased) current world. Since humans are biased, machine learning systems also inherit human’s biases. @galuhsahid
  54. Gender-neutral translations Myth #1: Machine learning is neutral Source @galuhsahid

  55. Gender-neutral translations Myth #1: Machine learning is neutral @galuhsahid

  56. Gender-neutral translations Myth #1: Machine learning is neutral @galuhsahid

  57. Text generation Myth #1: Machine learning is neutral Source @galuhsahid

  58. Myth #2 Machine learning is (or can be) perfect @galuhsahid

  59. Myth #2 Machine learning is (or can be) perfect Machine

    learning is never going to be perfect. Even humans can make errors. However, machine learning can improve. @galuhsahid
  60. Feedback loop Myth #2: Machine learning is (or can be)

    perfect @galuhsahid
  61. Feedback loop Myth #2: Machine learning is (or can be)

    perfect @galuhsahid
  62. Feedback loop Myth #2: Machine learning is (or can be)

    perfect @galuhsahid
  63. Feedback loop Myth #2: Machine learning is (or can be)

    perfect @galuhsahid
  64. Myth #3 We can just “build/apply ML” and call it

    a day @galuhsahid
  65. Myth #3 We can just “build/apply ML” and call it

    a day We still need to collaborate with others to ensure that we are solving the right problem. Machine learning is not going to figure out what the problem is. @galuhsahid
  66. Example: recommendation system Myth #3: We can just “apply ML”

    and call it a day
  67. Example: recommendation system Myth #3: We can just “apply ML”

    and call it a day @galuhsahid
  68. Demo @galuhsahid

  69. Rock Paper Scissors Demo @galuhsahid Code

  70. Scroll with Your Voice Demo @galuhsahid Code

  71. More machine learning • On building ML projects: First Steps

    Towards Your First Machine Learning Project • On ML with JavaScript: Machine Learning on the Web • On ML with TensorFlow: A Whirlwind Tour of Machine Learning with TensorFlow @galuhsahid
  72. References • On UX and ML: Human-Centered Machine Learning (article)

    • On machine learning bias: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (book) • On end-to-end example of building ML-powered products: Building Machine Learning Powered Applications (book) @galuhsahid
  73. Thank you! @galuhsahid