INDT Webinar about Applications of Machine Learning. In these slides Hayim Makabee presents several applications of Machine Learning and their impact on our lives, including Recommender Systems and Autonomous Vehicles, with several examples of recent innovations in the fields of Industry, Health and Agriculture.

1. Applications of
Machine
Learning
November/2020

2. About Me

3. Applications of
Machine Learning
Main applications of Machine
Learning, by type of problem:
• Clustering
• Classification
• Recommendation
3

4. Clustering
Goal:
Cluster observations into
meaningful groups.
4

5. Examples of Clustering
Applications
• “How to group customers for targeted
marketing purposes?”
• “Which neighborhoods in a country
are most similar to each other?”
• “What groups of insurance policy
holders have high claim costs?”
• “How to group the products in a store
based on their attributes?”
• “How to group pictures based on their
description?”
5

6. Classification
Goal:
Predict class from
observations.
6

7. Examples of Classification
Applications
• “Which category of products is most
interesting to this customer?”
• “Is this movie a romantic comedy,
documentary, or thriller?”
• “Is this review written by a customer
or a robot?”
• “Will the customer buy this product?”
• “Is this email spam or not spam?”
7

8. Recommendation
Goal:
Personalized
recommendation of items to
users.
8

9. Examples of
Recommendation
Applications
• “Which movies should be
recommended to a user?”
• “If the user just listened to a song,
which song would he like now?”
• “Which news articles are relevant for
a user in a particular context?”
• “Which advertisements should be
displayed for a user on a mobile
app?”
• “Which products are frequently
bought together?”
9

10. What do
these
companies
have in
common?

11. Amazon Website
Year 2000

12. The Netflix Challenge
On October 2006 Netflix offered a challenge to the data science community:
improve our recommendation algorithm by 10% and win a million dollars. In
September 2009 the winners were announced.

13. Are recommendations
effective?
• 35% of Amazon’s revenue is
generated by its
recommendation engine
• 60% of video clicks on
YouTube came from home
page recommendation
• 80% of movies watched on
Netflix came from
recommendations

14. Machine Learning
enables…
Automation
Optimization
Personalization
Increase Revenue AND
Improve User Experience
Create Addictive Experiences

15. Bayesian
Optimization

16. Israel is a leader
in Content
Recommendation

18. What do these Israeli companies have in
common?

19. Autonomous Vehicles
• Car-as-a-Service
• End of ownership
• Less vehicles
• End of parking lots
• End of taxi drivers
• End of gas stations
• Public transportation?
• Insurance industry
• Banking industry
• Symbol of status
• Form vs Function
• Less accidents

20. INDUSTRY 4.0

23. Machine
Learning
applied to
Agriculture

24. SeeTree will
provide
farmers with
important
information on
crops,
including
disease,
irrigation, and
fertility.

26. Machine
Learning
applied to
Health

30. Machine
Learning
applied to
Security

32. Machine Learning applied to Influencer Marketing
KashKlik

33. Recommender-System-as-a-Service
Sales Advancer

35. Avoiding Biases
The COMPAS algorithm for recidivism
prediction produces much higher false positive
rate for black people than white people.

36. China’s
Social
Credit
Score

38. Contact:
Hayim Makabee
Hayim@KashKlik.com