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Free and Open Machine Learning

Maikel Mardjan
Maikel Mardjan

Machine learning is and should not be the exclusive domain of large commercial companies, data scientists, mathematics, computer scientists or hackers. Our belief is that every business and everyone should be able to take advantage of the machine learning techniques and applications available.

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FREE AND OPEN MACHINE LEARNING 息 2018 Maikel Mardjan This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By Maikel Mardjan Tech Tuesday 12 June 2018
SCOPE Machine Learning How What Why Free and Open
Free and Open Machine Learning
WHOAMI Name : Maikel Mardjan Architecture & Design 24+ years working within IT Industry Master (MSc) Business Studies of University of Groningen Master degree (MSc) Electrical Engineering, of Delft University of Technology and still likes to do real hands-on programming (C/C++, Java, Python, PHP,JS,GOlang etc) to make and break things I love solving IT challenges and creating designs for complex systems. @maikelmardjan
AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
WHAT IS MACHINE LEARNING?
WHAT IS MACHINE LEARNING? In essence machine learning makes computers learn the same way people learn: Through experience. And just as with humans algorithms exist that makes it possible to make use of learned experience of other computers.
WHAT IS MACHINE LEARNING? Machine Learning can be defined as: A field of computer science that uses statistical techniques to give computer systems the ability to learn. So progressively improve performance on a specific task using data, without being explicitly programmed.
AI, ML AND DEEP LEARNING Artificial Intelligence Machine Learning Deep Learning
OPPORTUNITIES?! What would you like to create? What problem do you like to solve?
EXAMPLE APPLICATIONS OF ML: HEALTHCARE
EXAMPLE APPLICATIONS OF ML: LANGUAGE TRANSLATION (SMART)
EXAMPLE APPLICATIONS OF ML: ECOMMERCE RECOMMENDATION SYSTEMS
EXAMPLE APPLICATIONS OF ML: CHAT BOTS
EXAMPLE APPLICATIONS OF ML Quality inspection and improvement Vision (E.g. Face detection, Object Detection, Image classification)V Security (Fraud detection, Surveillance, Spam filters, Network Intrusion Detection)
AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
THE PARADIGM SHIFT: CREATING SMART SOFTWARE Traditional programming vs Machine Learning Computer (Traditional programming) Input Create Program Output
THE PARADIGM SHIFT: CREATING SMART SOFTWARE Traditional programming vs Machine Learning Computer (Machine Learning) Input Output Learning program New input New Output
SO IT IS NOT PROGRAMMING With ML you can create (program) a cat detector by providing your machine learning system many examples of cats and dogs. cat cat dog dog The more cats you feed your ML algorithm, the better your outcome will be! cat dogdog cat
MUST HAVE FOR ML For machine learning, four things are needed: Data. More is better. A model of how to transform the data. A loss function to measure how good the model is performing. An algorithm to tweak the model parameters such that the loss function is minimized
DATA FOR MACHINE LEARNING Images Text Video Structured data (E.g. Webpages, electronic medical records, car, electricity bills, etc.) More = Better
ML WORKING: IT CAN BE SUPER COMPLEX Machine Learning Supervised task driven (Regression / Classification) Unsupervised Data Driven (Clustering) Reinforcement (Algorithms learning from environment)
NEURAL NETWORKS (NNS) Neural networks (NNs) can be defined as: Algorithms in machine learning that are implemented by using the structure of neural networks. Neural networks model the data using artificial neurons. So Neural networks thus mimic the functioning of the human brain. A brains neural networks continuously change and update themselves in many ways. This happens as a direct result of learning and experience.
NEURAL NETWORKS
NEURAL NETWORKS
KNOWLEDGE Defining knowledge is hard, but crucial for many machine learning applications. An attempt to define knowledge in the context of ML: The ability of a computer to reason by understanding the relationship between people, things, places, events and context.
AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
WHAT IS OPEN MACHINE LEARNING?
KNOW YOUR ALGORITHM
WHAT IS FREE MACHINE LEARNING?
MACHINE LEARNING AND RISKS
Free and Open Machine Learning
MACHINE LEARNING AND RISKS Safety Security Privacy
AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
PROBLEMS AND CHALLENGES FOR FREE AND OPEN MACHINE LEARNING
PROBLEMS AND CHALLENGES FOR FREE AND OPEN MACHINE LEARNING Open science Open data Open access Open research Open Source Software Culture Change Commercial interest Economics Knowledge Awareness
THE BASE WORK WITH MACHINE LEARNING Important Interesting Who Cares? Is ML really needed? Is anyone interested at all? If it is not important, not interesting and delivers no value: Do not do it! Exploratory Fundamentally interesting problems. Machine learning Could help to solve it. Find people who like to play with this problem. Shit Work Crucial for doing the crucial problem solving work with ML. A good fundament based on a solid architecture, infrastructure, development pipeline will always deliver value later. High Value Applying ML delivers value. Professionals and companies like interesting & important projects when developing applications using ML.
SUMMARY AND RECAP What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
THANK YOU! Support Free and Open Machine Learning Contribute to Free and Open Machine Learning Book Check on: https://www.bm-support.org/projects/ More information? Call me : +31 [0] 6 22869536 of Mail : info@organisatieontwerp.nl Twitter : @maikelmardjan Also available for solving your real nasty complex IT problems! https://nocomplexity.com/
ABOUT BM-SUPPORT.ORG The Business Management Support Foundation is a not for profit organization for radical open business innovation. The purpose of the foundation is to stimulate and perform research and development on the broad field of system sciences and practical applications. We do this by creating open innovation networks with other non profit organizations and profit organizations. The BM-Support.org foundation is devoted to the interdisciplinary inquiry into the nature of complex systems. The foundation is created in 2007 to support research and development of innovation projects. Check https://www.bm-support.org for more information!
ITS ALL ABOUT COLLABORATION!

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Free and Open Machine Learning

  • 1. FREE AND OPEN MACHINE LEARNING 息 2018 Maikel Mardjan This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By Maikel Mardjan Tech Tuesday 12 June 2018
  • 4. WHOAMI Name : Maikel Mardjan Architecture & Design 24+ years working within IT Industry Master (MSc) Business Studies of University of Groningen Master degree (MSc) Electrical Engineering, of Delft University of Technology and still likes to do real hands-on programming (C/C++, Java, Python, PHP,JS,GOlang etc) to make and break things I love solving IT challenges and creating designs for complex systems. @maikelmardjan
  • 5. AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
  • 6. WHAT IS MACHINE LEARNING?
  • 7. WHAT IS MACHINE LEARNING? In essence machine learning makes computers learn the same way people learn: Through experience. And just as with humans algorithms exist that makes it possible to make use of learned experience of other computers.
  • 8. WHAT IS MACHINE LEARNING? Machine Learning can be defined as: A field of computer science that uses statistical techniques to give computer systems the ability to learn. So progressively improve performance on a specific task using data, without being explicitly programmed.
  • 9. AI, ML AND DEEP LEARNING Artificial Intelligence Machine Learning Deep Learning
  • 10. OPPORTUNITIES?! What would you like to create? What problem do you like to solve?
  • 11. EXAMPLE APPLICATIONS OF ML: HEALTHCARE
  • 12. EXAMPLE APPLICATIONS OF ML: LANGUAGE TRANSLATION (SMART)
  • 13. EXAMPLE APPLICATIONS OF ML: ECOMMERCE RECOMMENDATION SYSTEMS
  • 14. EXAMPLE APPLICATIONS OF ML: CHAT BOTS
  • 15. EXAMPLE APPLICATIONS OF ML Quality inspection and improvement Vision (E.g. Face detection, Object Detection, Image classification)V Security (Fraud detection, Surveillance, Spam filters, Network Intrusion Detection)
  • 16. AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
  • 17. THE PARADIGM SHIFT: CREATING SMART SOFTWARE Traditional programming vs Machine Learning Computer (Traditional programming) Input Create Program Output
  • 18. THE PARADIGM SHIFT: CREATING SMART SOFTWARE Traditional programming vs Machine Learning Computer (Machine Learning) Input Output Learning program New input New Output
  • 19. SO IT IS NOT PROGRAMMING With ML you can create (program) a cat detector by providing your machine learning system many examples of cats and dogs. cat cat dog dog The more cats you feed your ML algorithm, the better your outcome will be! cat dogdog cat
  • 20. MUST HAVE FOR ML For machine learning, four things are needed: Data. More is better. A model of how to transform the data. A loss function to measure how good the model is performing. An algorithm to tweak the model parameters such that the loss function is minimized
  • 21. DATA FOR MACHINE LEARNING Images Text Video Structured data (E.g. Webpages, electronic medical records, car, electricity bills, etc.) More = Better
  • 22. ML WORKING: IT CAN BE SUPER COMPLEX Machine Learning Supervised task driven (Regression / Classification) Unsupervised Data Driven (Clustering) Reinforcement (Algorithms learning from environment)
  • 23. NEURAL NETWORKS (NNS) Neural networks (NNs) can be defined as: Algorithms in machine learning that are implemented by using the structure of neural networks. Neural networks model the data using artificial neurons. So Neural networks thus mimic the functioning of the human brain. A brains neural networks continuously change and update themselves in many ways. This happens as a direct result of learning and experience.
  • 26. KNOWLEDGE Defining knowledge is hard, but crucial for many machine learning applications. An attempt to define knowledge in the context of ML: The ability of a computer to reason by understanding the relationship between people, things, places, events and context.
  • 27. AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
  • 30. WHAT IS FREE MACHINE LEARNING?
  • 33. MACHINE LEARNING AND RISKS Safety Security Privacy
  • 34. AGENDA What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
  • 35. PROBLEMS AND CHALLENGES FOR FREE AND OPEN MACHINE LEARNING
  • 36. PROBLEMS AND CHALLENGES FOR FREE AND OPEN MACHINE LEARNING Open science Open data Open access Open research Open Source Software Culture Change Commercial interest Economics Knowledge Awareness
  • 37. THE BASE WORK WITH MACHINE LEARNING Important Interesting Who Cares? Is ML really needed? Is anyone interested at all? If it is not important, not interesting and delivers no value: Do not do it! Exploratory Fundamentally interesting problems. Machine learning Could help to solve it. Find people who like to play with this problem. Shit Work Crucial for doing the crucial problem solving work with ML. A good fundament based on a solid architecture, infrastructure, development pipeline will always deliver value later. High Value Applying ML delivers value. Professionals and companies like interesting & important projects when developing applications using ML.
  • 38. SUMMARY AND RECAP What is Machine Learning How does machine learning work (Simplified) What is Free and Open Problems and challenges for Free and Open Machine Learning
  • 39. THANK YOU! Support Free and Open Machine Learning Contribute to Free and Open Machine Learning Book Check on: https://www.bm-support.org/projects/ More information? Call me : +31 [0] 6 22869536 of Mail : info@organisatieontwerp.nl Twitter : @maikelmardjan Also available for solving your real nasty complex IT problems! https://nocomplexity.com/
  • 40. ABOUT BM-SUPPORT.ORG The Business Management Support Foundation is a not for profit organization for radical open business innovation. The purpose of the foundation is to stimulate and perform research and development on the broad field of system sciences and practical applications. We do this by creating open innovation networks with other non profit organizations and profit organizations. The BM-Support.org foundation is devoted to the interdisciplinary inquiry into the nature of complex systems. The foundation is created in 2007 to support research and development of innovation projects. Check https://www.bm-support.org for more information!
  • 41. ITS ALL ABOUT COLLABORATION!