The document discusses the various applications of machine learning, including clustering, classification, and recommendation, along with examples of each application type. It highlights the effectiveness of recommendation systems in generating revenue and improving user experiences, as seen in companies like Amazon and Netflix. Additionally, it addresses the failures of machine learning in terms of biased outcomes and discrimination.

1. Applications
of Machine
Learning
BRIL Chamber
Câmara Brasil Israel de
Comércio e Indústria
September/2020

2. Applications of
Machine Learning
Main applications of Machine
Learning, by type of problem:
• Clustering
• Classification
• Recommendation
2

3. Clustering
Goal:
Cluster observations into
meaningful groups.
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4. 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?”
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5. Classification
Goal:
Predict class from
observations.
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6. 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?”
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7. Recommendation
Goal:
Personalized
recommendation of items to
users.
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8. 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?”
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9. Failures of Machine
Learning
• In May 2015 Flickr released an
automatic image tagging capability
that mistakenly labeled a black man
for an ape.
• Soon afterwards, Google came up with
a photo labeling tool similar to Flickr,
which made similar mistakes. Black
men were tagged as gorillas.
• A recent Carnegie Mellon University
study showed that Google displayed
ads in a way that discriminated based
on the gender of the user.
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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. Israel is a leader
in Content
Recommendation

16. What do these Israeli companies have in
common?

17. 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

18. Machine Learning applied to Influencer Marketing
KashKlik

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

20. Contact:
Hayim Makabee
Hayim@KashKlik.com