Applications of Machine Learning - INDT Webinar
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The document outlines the main applications of machine learning, categorizing them into clustering, classification, and recommendation, with various examples for each type. It also highlights the significant impact of recommendation systems on revenue and user engagement for companies like Amazon and Netflix. Furthermore, the document touches on the implications of machine learning in various industries, including agriculture, health, and security, while also addressing potential biases in algorithms.
Applications of Machine Learning - INDT Webinar
- 2. About Me
- 3. Applications of Machine Learning Main applications of Machine Learning, by type of problem: • Clustering • Classification • Recommendation 3
- 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
- 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
- 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
- 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
- 24. SeeTree will provide farmers with important information on crops, including disease, irrigation, and fertility.
- 32. Machine Learning applied to Influencer Marketing KashKlik
- 35. Avoiding Biases The COMPAS algorithm for recidivism prediction produces much higher false positive rate for black people than white people.