[Edin Kapič] Machine Learning (ML) is the new buzzword for the future of computing, together with Big Data and Quantum Computing. But, what is it? What are its strengths and weaknesses? Finally, why should you care (or not) for ML in your collaboration technology stack? In this marketing-free session you will get an overview of the ML and the major technology vendors, with heavy accent on Microsoft and Azure ML offerings. Also, as a technical person (developer, administrator or IT manager) you will get a rough idea of the solution pieces, infrastructure and languages. After the session, you will have a clearer picture and you'll be able to assess whether you should invest in ML in your own area of responsibility.

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@ekapic

4. Marina Amaral

7. http://www.marinamaral.com/portfolio-2/

8. http://hi.cs.waseda.ac.jp:8082/

10. What Why How But

11. What Why How But

12. Machine Learning

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Alice
Foo
Baz
Bar
Bob
Doo

16. All examples are from https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-choice
Clustering
(what groups of customers I have?)
Recommendation
(what song should I listen to?)
Regression
(how many sales am I making?)
Classification
(what is in this picture?)
Anomaly detection
(is this account activity normal?)

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bcned.in/AzureMLCheatsheet bcned.in/AzureMLInfographic

23. What Why How But

24. Predict
some
future
behavior

25. Quickly do
time-
consuming
classification

26. Aid human
experts

27. React to
changes in
real time

28. Perform
non-
scriptable
operations

29. ML in
Collaboration

30. What Why How But

32. Microsoft Cognitive
Services
Amazon Rekognition

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36. Microsoft ML Studio
Amazon SageMaker

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41. CNTK
WinML
TensorFlow

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https://www.microsoft.com/en-us/cognitive-toolkit/

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https://docs.microsoft.com/en-us/windows/uwp/machine-learning/

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https://www.tensorflow.org/

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47. What Why How But

48. Correlation != Causation

49. Limitations of Machine Learning

51. Ethical Concerns

52. Privacy Concerns

53. Hype and Expectations

54. What
• Pattern recognition
• Supervised
• Unsupervised
Why
• Prediction
• Realtime
• Time-consuming
How
• Services
• Platforms
• Libraries
But
• Ethics
• Privacy
• Limitations
Machine Learning