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Machine Learning - What it is and why you should care
The document discusses machine learning as a key area of artificial intelligence focused on autonomous pattern identification and detection. It outlines its applications, such as computer vision and natural language processing, while addressing misconceptions and emphasizing that there is no imminent singularity from AI advancements. The document provides insights on how to get started with machine learning and the necessary skills and tools involved in the process.
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Machine Learning - What it is and why you should care
- 1. Machine Learning What itis and why you should care Dr. Christian Wiele @christian_wiele
- 2. ©2017 gezeitenraum What isMachine Learning? 2
- 3. ©2017 gezeitenraum Machine Learningis the predominant area of artificial Intelligence (often AI and ML are used interchangeably) 3
- 4. ©2017 gezeitenraum Machine Learning = autonomouspattern identification and detection 4
- 5. ©2017 gezeitenraum Will wesoon be dominated by super-human machines? 5
- 6. ©2017 gezeitenraum There areabsolutely no indications of a nearby singularity! 6
- 7. ©2017 gezeitenraum Why shouldyou care about Machine Learning? 7
- 8. Is it morethan a hype? ©2017 gezeitenraum 8
- 9. ML gets theValue 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 beover 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 ofMachine Learning ©2017 gezeitenraum 11 apply ML as black box understand research
- 12. ©2017 gezeitenraum So, Whatis Machine learning about? 12
- 13. Core Challenge ofMachine learning ©2017 gezeitenraum 13 What humans are good at What Machines are good at (73492.232 + 2049.3827) * 883792.45
- 14. Learning approaches ©2017 gezeitenraum14 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
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- 16. ©2017 gezeitenraum How itworks 16
- 17. Classical programming paradigm ©2017gezeitenraum 17 If (pixel1 == … And Pixel2 == …) Then … Else IF …
- 18. Goal: minimize theerror ©2017 gezeitenraum 18 error {Parameter 1 Parameter 2
- 19. The fruit flyof ML: MNIST ©2017 gezeitenraum 19 55.000 handwritten digits 28x28 pixels
- 20. Neural Net: digitrecognition ©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: Internalview ©2017 gezeitenraum 21 logic: result = one number Output: 1 = activate 0 = not activate
- 22. Activation ©2017 gezeitenraum 22 hiddenneuron 12: red dominantes —> fire input mask / weights weighted input hidden neuron 13: blue dominantes —> don’t fire
- 23. Neural Net: digitrecognition ©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 relevantpixel structures? ©2017 gezeitenraum 24 neuron 12 neuron 18 neuron 25
- 25. ©2017 gezeitenraum So, howdoes learning work? 25
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- 27. Neural Net: LearningProcess ©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 toget stared with Machine Learning? 28
- 29. Skill Levels ofMachine Learning ©2017 gezeitenraum 29 apply ML as black box understand research
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- 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 learningexpert ©2017 gezeitenraum 33
- 34. Geschafft ©2016 gezeitenraum 34 VielenDank! Dr. Christian Wiele christian@gezeitenraum.com @christian_wiele @gezeitenraum