Machine Learning for Managers - ML for Executives Course. DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

1. Jan W. Veldsink MSc

2. Co-organized by: Sponsored by:
Business Partners:

3. Jan W. Veldsink MSc
1rd edition | July 8 - 11, 2019

4. Jan W. Veldsink MSc
THE ART OF AI
AI - DEMYSTIFIED
Jan W Veldsink MSc

5. Jan W. Veldsink MSc

6. Jan W. Veldsink MSc
Guidelines from the DNB

7. Jan W. Veldsink MSc
AXVECO
Technology
Business
model redesign
Governance,
risk & compliance
Competences for sustainable innovation
• Business ecosystem
• Value chain analysis
• Revenue model
• Customer experience
• Process design and improvement
• Business rules/logic
• Systems architecture with new
technologies
• Big Data Analysis, Robotics and AI
• Blockchain
• Smart contracts and dApp development
• GUI, legacy systems, infrastructure
• Operational risk, including privacy,
cyber-security, business continuity
• Regulation and compliance
• Legality
• Governance implications
• Culture
• Leadership
• Capacity to innovate
• (Corporate) venturing
• Product development
• Agile, Lean Start up, design
thinking
• Business case development

8. Jan W. Veldsink MSc
Perceived benefits from AI

9. Jan W. Veldsink MSc
Perceived benefits from AI

10. Jan W. Veldsink MSc
“THOSE WHO RULE DATA WILL
RULE THE WORLD”

11. Jan W. Veldsink MSc
CHALLENGES OF AI

12. Jan W. Veldsink MSc
Machine Learning
Machine Learning is the steam engine of the XXI century
When applied methodically, it helps solve complex problems at human-level
performance
When applied systematically, it can dramatically improve the performance
of an organization (of all sizes and in all sectors).

13. Jan W. Veldsink MSc
Machine Learning Evolution
Genesis
Custom built
Product Service
Utility
Academics & Researchers
Scientists
Developers
Analysts
Everyone
1950s
1980s
2000s
2012
2030
Commodity
2020
Ubiquity
CertaintyUnknown Defined
NovelCommon
Weka, R, Scikit, H2O
BigML, Azure ML,
Amazon ML,
Google Cloud ML
1st
Workshop on Machine
Learning

14. Jan W. Veldsink MSc
Predictive Services
• Every initiative is going to involve AI
• Every initiative is driven by DATA
• Every initiative is MODEL BASED
•Every initiative is realised NON CODING

15. Jan W. Veldsink MSc
Machine Learning
Inputs
Program
Outputs
Traditional Programming
Inputs
Program
Outputs
Machine Learning
It’s a CAT Label: Cat, dog, horse, spider
It’s another CAT

16. Jan W. Veldsink MSc
Source of inspiration

17. Jan W. Veldsink MSc

18. Jan W. Veldsink MSc
Source of Inspiration
SYM
BOLIC AI
CONNECTIONIST AI
Senses
Classification
Recognition
Symbols
Concepts
Language
Reasoning
Planning

19. Jan W. Veldsink MSc
Machine learned models

20. Jan W. Veldsink MSc
Where to Start?
Step
1 Finish
“Let’s predict
the Oscars!” “Here are the
predicted winners”
Step
2
- - - - - - - -
???

21. Jan W. Veldsink MSc
Where to Start?
Step
1
Finish
“Let’s detect
fraud!
“Here are the
transactions we should
stop immediately.
Step
2
- - - - - - - -
???

22. Jan W. Veldsink MSc
Where to Start?
Step
1
Finish
“Let’s predict
customer churn!”
“Here are the
customers we predict
will leave our service”
Step
2
- - - - - - - -
???

23. Jan W. Veldsink MSc
Where to Start?
Step
1
Finish
“Let’s predict
diabetes!” “Does a person have an
indication for diabetes”
Step
2
- - - - - - - -
???

24. Jan W. Veldsink MSc

25. Jan W. Veldsink MSc
BIGML meets Alexa & Lynx
“What is the BMI?”
“The possibility that you
become obese is 21,7%”
“19”

26. Jan W. Veldsink MSc
Decision tree – Diabetes classification

27. Jan W. Veldsink MSc
TRENDS AND OBSERVATIONS

28. Jan W. Veldsink MSc
ML Results on fraud

29. Jan W. Veldsink MSc
Decision engineering

30. Jan W. Veldsink MSc
Decision engineering
• Decision intelligence is an engineering discipline that augments data
science with theory from social science, decision theory, and managerial
science.
• Its application provides a framework for best practices in organizational
decision-making and processes for applying machine learning at scale.
https://en.wikipedia.org/wiki/Decision_Intelligence

31. Jan W. Veldsink MSc
Decision engineering
ML/AI expert
Domain Data
expert
Business Domain
expert

32. Jan W. Veldsink MSc
Some Rabobank Projects
DataDrift
Agri Default Prediction
Fraud
CDD - Anomalies

33. Jan W. Veldsink MSc
For example Current projects
Some statistics:
Number of users 85
Most non IT staff
:
•Fraud
•AML
•CDD
•KYC
•Credit Risk
•Compliance auditing
•Data integrity / Datadrift
•Sanctions
•Real estate dynamic pricing
•Privacy violations

34. Jan W. Veldsink MSc
Core Quesbons
Viable
How realistic is applying AI in this situation?
(Is there a AI technology available for the current question)
Valuable Does applying AI add value to customers / users?
Vital
How crucial is AI for achieving the result?
AI / ML technology assessment

35. Jan W. Veldsink MSc

36. Jan W. Veldsink MSc
THE ART
OF AI
AI - DEMYSTIFIED
JAN VELDSINK MSC
JAN@GRIO.NL
J.VELDSINK@NYENRODE.NL

37. Jan W. Veldsink MSc