Introduction to Machine Learning (UX Indonesia Meetup)

For UX ID Meetup

Transcript

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