Machine Learning on Mobile—a primer

Machine learning is incredibly powerful, and with the introduction of Core ML on iOS and ML Kit on Android, finally becoming usable on mobile devices. But what exactly is 'machine learning'.
In this talk you'll hear about the basics of machine learning—the fact that at its simplest level it's just trivial mathematical functions. We'll build up our own models in the talk, using principles from high school mathematics, demonstrating that machine learning isn't as complex as it may seem.
We'll then look at how to work with more advanced models, including convolutional neural networks (CNNs) and how they work in iOS with Core ML.
At the end of the talk you will have a better understanding of exactly what 'machine learning' is, along with some practical knowledge of how to use machine learning in their own apps.

Transcript

1. MACHINE LEARNING ON MOBILE: A primer @iwantmyrealname
2. CORE ML @iwantmyrealname
3. With Core ML, you can integrate trained machine learning models into your app. — developer.apple.com @iwantmyrealname
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6. CLASSIFICATION tell me which of these groups the input falls into @iwantmyrealname
7. REGRESSION estimate an output value given this input @iwantmyrealname
8. CLUSTERING how many different classes does the data fall into? @iwantmyrealname
9. 1 COLLECT DATA @iwantmyrealname
10. 2 GROUND TRUTH @iwantmyrealname
11. 3 FEATURE VECTORS @iwantmyrealname
12. 4 TRAIN MODEL @iwantmyrealname
13. 5 TEST MODEL @iwantmyrealname
14. 6 REPEAT 3, 4 & 5 @iwantmyrealname
15. 7 DEPLOY MODEL @iwantmyrealname
16. 8 PERFORM PREDICTION @iwantmyrealname
17. 1. Collect data 2. Ground truth 3. Feature vectors 4. Train model 5. Test model 6. Repeat 3, 4 & 5 7. Deploy model 8. Perform prediction @iwantmyrealname
18. 1. Collect data 2. Ground truth 3. Feature vectors 4. Train model 5. Test model 6. Repeat 3, 4 & 5 7. Deploy model 8. Perform prediction @iwantmyrealname
19. 1. Collect data 2. Ground truth 3. Feature vectors 4. Train model 5. Test model 6. Repeat 3, 4 & 5 7. Deploy model @iwantmyrealname
20. LINEAR REGRESSION SUPPORT VECTOR MACHINE Tree Ensembles BAYESIAN NETWORKS Neural Networks @iwantmyrealname
21. CORE ML PROVIDES AN abstraction ACROSS different MODEL TYPES @iwantmyrealname
22. MODEL INTERCHANGE format @iwantmyrealname
23. DEVICE OPTIMISED implementation @iwantmyrealname
24. CONSISTENT API @iwantmyrealname
25. THAT JUST LEAVES THE hard PART @iwantmyrealname
26. SVM SUPPORT VECTOR MACHINE @iwantmyrealname
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40. example: SENTIMENT ANALYSIS @iwantmyrealname
41. bag OF words @iwantmyrealname
42. DEMO @iwantmyrealname
43. CONVOLUTIONAL NEURAL NETWORKS @iwantmyrealname
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57. example: SALIENT OBJECT SUBITIZING @iwantmyrealname
58. DEMO @iwantmyrealname
59. Create ML @iwantmyrealname
60. macOS FRAMEWORK @iwantmyrealname
61. LIMITED but POWERFUL @iwantmyrealname
62. DEMO @iwantmyrealname
63. CONCLUSION @iwantmyrealname
64. Core ML IS A small PART OF THE STORY @iwantmyrealname
65. useful NONETHELESS @iwantmyrealname
66. FORMAT optimisation API @iwantmyrealname
67. POTENTIALLY exciting TIMES @iwantmyrealname
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69. github.com/sammyd /MOBILEOPTIMISED2018_ML SAM@RAZEWARE.COM @IWANTMYREALNAME @iwantmyrealname