Machine Learning - What it is and why you should care
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An introduction to machine learning - what it is and why you should care. Presentation from #Waterkant SH Startup Festival 2017.
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Machine Learning - What it is and why you should care
- 1. Machine Learning What it is and why you should care Dr. Christian Wiele @christian_wiele
- 2. ©2017 gezeitenraum What is Machine Learning? 2
- 3. ©2017 gezeitenraum Machine Learning is the predominant area of artificial Intelligence (often AI and ML are used interchangeably) 3
- 4. ©2017 gezeitenraum Machine Learning = autonomous pattern identification and detection 4
- 5. ©2017 gezeitenraum Will we soon be dominated by super-human machines? 5
- 6. ©2017 gezeitenraum There are absolutely no indications of a nearby singularity! 6
- 7. ©2017 gezeitenraum Why should you care about Machine Learning? 7
- 8. Is it more than a hype? ©2017 gezeitenraum 8
- 9. ML gets the Value out of Data ©2017 gezeitenraum Computer vision ➡e.g. autonomous driving, cancer detection Speech recognition ➡e.g. Siri Natural language processing (NLP) ➡machine translation, chat bots General patterns ➡click stream analysis, fraud detection, security, recommender systems 9
- 10. AI might be over in EU before it starts ©2017 gezeitenraum 10 http://www.techzone360.com/topics/techzone/articles/2017/01/25/429101-eus-right-explanation-harmful-restriction-artificial-intelligence.htm
- 11. Skill Levels of Machine Learning ©2017 gezeitenraum 11 apply ML as black box understand research
- 12. ©2017 gezeitenraum So, What is Machine learning about? 12
- 13. Core Challenge of Machine learning ©2017 gezeitenraum 13 What humans are good at What Machines are good at (73492.232 + 2049.3827) * 883792.45
- 14. Learning approaches ©2017 gezeitenraum 14 supervised learning unsupervised learning BananA Grape Banana X X X X X X X X X X X X X X X X X X X X X X XX X X X
- 16. ©2017 gezeitenraum How it works 16
- 17. Classical programming paradigm ©2017 gezeitenraum 17 If (pixel1 == … And Pixel2 == …) Then … Else IF …
- 18. Goal: minimize the error ©2017 gezeitenraum 18 error {Parameter 1 Parameter 2
- 19. The fruit fly of ML: MNIST ©2017 gezeitenraum 19 55.000 handwritten digits 28x28 pixels
- 20. Neural Net: digit recognition ©2017 gezeitenraum 20 Input 1 2 3 4 5 6 7 8 9 0 output neurons 2 3 4 5 6 7 8 9 10 1 12 2 3 17 18 1 25 24 ……… … … hidden neurons output
- 21. Artificial Neuron: Internal view ©2017 gezeitenraum 21 logic: result = one number Output: 1 = activate 0 = not activate
- 22. Activation ©2017 gezeitenraum 22 hidden neuron 12: red dominantes —> fire input mask / weights weighted input hidden neuron 13: blue dominantes —> don’t fire
- 23. Neural Net: digit recognition ©2017 gezeitenraum 23 Input Output-Neuronen 1 2 3 4 5 6 7 8 9 0 2 3 4 5 6 7 8 9 10 1 … 12 2 3 17 18 1 25 24 ……… … Innere-Neuronen Output
- 24. What are relevant pixel structures? ©2017 gezeitenraum 24 neuron 12 neuron 18 neuron 25
- 25. ©2017 gezeitenraum So, how does learning work? 25
- 27. Neural Net: Learning Process ©2017 gezeitenraum 27 Input Output-Neuronen 2 3 4 5 6 7 8 9 10 1 12 2 3 17 18 1 25 24 ……… … … Innere-Neuronen Output 1 2 3 4 5 6 7 8 9 0 error error error slightly change parameters
- 28. ©2017 gezeitenraum How to get stared with Machine Learning? 28
- 29. Skill Levels of Machine Learning ©2017 gezeitenraum 29 apply ML as black box understand research
- 32. Toolset ©2017 gezeitenraum Python ➡Theano, Tensorflow, Keras, NumPy, … ➡Jupyter notebook Linear algebra Access to GPU ➡AWS, Google, or your own … Time and motivation for experimenting 32
- 33. The machine learning expert ©2017 gezeitenraum 33
- 34. Geschafft ©2016 gezeitenraum 34 Vielen Dank! Dr. Christian Wiele christian@gezeitenraum.com @christian_wiele @gezeitenraum