[Kapič] Why You Should (Not) Probably Care about Machine Learning

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[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.

Software

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?)

• •
bcned.in/AzureMLCheatsheet bcned.in/AzureMLInfographic

Quickly do
time-
consuming
classification

Microsoft Cognitive
Services
Amazon Rekognition

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

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

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

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