This document provides an overview of machine learning, focusing on its definition, how it works, and the principles of free and open machine learning. It outlines essential components required for machine learning and discusses various applications and challenges associated with it. Additionally, it emphasizes the importance of collaboration and open innovation in advancing machine learning research and development.

1. FREE AND OPEN MACHINE
LEARNING
© 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

2. SCOPE
Machine
Learning
How
What
Why
Free
and
Open

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 brain’s neural networks continuously change and update
themselves in many ways. This happens as a direct result of
learning and experience.

24. NEURAL NETWORKS

25. NEURAL NETWORKS

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

28. WHAT IS OPEN
MACHINE
LEARNING?

29. KNOW YOUR ALGORITHM

30. WHAT IS FREE MACHINE
LEARNING?

31. MACHINE LEARNING AND RISKS

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. IT’S ALL ABOUT COLLABORATION!