This document is a 10-part series from Predictive Layer on machine learning. It defines machine learning, compares traditional programming to machine learning, and provides examples of business uses cases for machine learning like predictive maintenance, demand forecasting, and dynamic pricing. It emphasizes that machine learning can generate adaptive applications and models in real-time from data to improve business performance without extensive programming. The series advocates that combining people and machine learning in teams can produce creative, productive, and efficient decision making.

1. Predictive Layer “Keep It Simple Series” Kiss 2018
“Machine Learning”
A series of 10 episodes
Credits /need to thank :
Andew Ng – Standford class of Machine Learning
Eric Grimson - MIT Class Machine Learning
PredictiveLayers Gurus
© Predictive Layer 2018

2. Arthur Samuel's definition from 1959:
Machine Learning: Field of study that gives
computers the ability to learn without being
explicitly programmed
Cognitive Computing
Keep It Simple Smart - Machine Learning #1/10
© Predictive Layer 2018

3. Tom Mitchell’s definition in 1998:
Well posed Learning Problem: A computer
program is said to learn from experience E with
respect to some task T and some performance
measure P, if its performance on T, as measured
by P, improves with experience E.
Cognitive Computing
Keep It Simple Smart - Machine Learning #2/10
© Predictive Layer 2018

4. Keep It Simple Smart - Machine Learning #3/10
The magic of Machine Learning :
generate programs based on data
that enable a totally adaptive set of
business applications that can serve
a truly agile business strategy.
In a way, Machine Learning generates
on a real time basis a new component
of your IS based on internal and
external data without depending on
massive complex expensive software
development or configuration.
It is a major game changer for
business
Credits /need to thank: Eric Grimson - MIT Class Machine Learning
Traditional Programming
Machine Learning
Program
Data
OutPut
Big Data
Program
Computing
Platform
OutPut
Computing
Platform
© Predictive Layer 2018

5. Keep It Simple Smart - Machine Learning #4/10
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6. Keep It Simple Smart - Machine Learning #4 /10 – Add on
Comparison of the expected revenue from advanced machine learning
(reinforced with with eligibility trace) as a function of the number of episodes (ie
weeks for transportation)
Advanced Machine Learning is a powerful game changer for yield and
revenue management and provide impressive financial returns
Correlation of historical big data (tickets, price, timestamp,
route and timetable, client profiles, airplane, options & extras)
and external influence data (weather, vacation, economics,
strikes, events, calendar & seasons, location, transport price
index etc.) to generate automatically predictive and
prescriptive models for volume, value and pricing on a real
time basis that outperform all other approaches
Predictive Layer Software Solution for Yield Management
© Predictive Layer 2018
From “ Real-Time Dynamic Pricing in a Non-Stationary
Environment using Model-Free Reinforcement Learning”

7. Mega Trend For The Next 10 Years:
COGNIFICATION OF BUSINESS
NEXT(X) = X + AI*
(*) AI: Artificial Intelligence , Machine Learning, Deep Learning
(*) X: Anything related to business performance, issue, challenge or process
Keep It Simple Smart - Machine Learning #5/10
© Predictive Layer 2018

8. FUTURE OF BUSINESS
=
CURRENT + USE CASE (ARTIFICIAL INTELLIGENCE*)
(*) AI: Artificial Intelligence , Machine Learning, Deep Learning (mostly for image and language)
Keep It Simple Smart - Machine Learning #6/10
SAMPLE BUSINESS USE CASES (MACHINE LEARNING)
Dynamic Demand Planning Dynamic Forecasting / Forecast to plan
Dynamic Pricing / Yield Management (Prescriptive) Dynamic Customer Segmentation
Predictive Maintenance (Prescriptive) Predictive Usage of Resources & Assets
Predictive Trafic for people, passengers or vehicles Prescriptive Workforce planning
Risk Management Credit Scoring
© Predictive Layer 2018

9. Keep It Simple Smart - Machine Learning #7/10
Select the right use case that creates value!
Align use case with Company Strategy and Purpose…
Value created should then become « obvious »
« Best Price For our Us and our Client » Yields Mngt / Dynamic Pricing (B2B and B2C)
« No queing for eating » Demand Planning & Forecasting
« The right product at the right place »
« No down time » Predictive Maintenance
« Less Trade & Promotion » Dynamic Forecasting and prescriptive A&P
© Predictive Layer 2018

10. TEAM of ARTIFICIAL INTELLIGENCE
PEOPLE & MACHINE LEARNING
CREATIVE Thinking PRODUCTIVE
DECISION Making EFFICIENT
Quality Time Time pressure
BEST TEAM
Keep It Simple Smart - Machine Learning #8/10
© Predictive Layer 2018

11. AI Positive Loop
AI/ML
Product
Users
Initial Data Set
Defendable Business Position With unique
data assets initially build with MVP.
Then expand with users (Data
Accumulation as a core Capability), and
use cases.
Big data & Open Data with AI/ML become a
critical differentiator for the enterprise
More
data
Keep It Simple Smart - Machine Learning #9/10
Using AI for sustainable business differentiation
Example: Yield Management / Dynamic Pricing
Value
MVP (Minimum Viable Product)AI/ML: Artifcial Intelligence & Machine Learning
© Predictive Layer 2018

12. After the industrial revolution of artificial physical power
that is evolving dramatically with the shift from fossile to
renewable energy
Time has come to expand with the revolution of artificial
intelligence and brain power that will disrupt business
and society
THE TIME IS NOW !
Keep It Simple Smart - Machine Learning #10/10
© Predictive Layer 2018

13. Credits /need to thank :
Andew Ng – Standford class of Machine Learning
Eric Grimson - MIT Class Machine Learning
PredictiveLayers Gurus
Predictive Layer “Keep It Simple Series” Kiss 2018
“Machine Learning”
A series of 10 episodes
Thank You
© Predictive Layer 2018