Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Julian McAuley has been a Professor at UC San Diego since 2014. Personalized Machine Learning is the main research area of his lab, with applications ranging from personalized recommendation, to dialog, healthcare, and fashion design. He regularly collaborates with industry on these topics, including with Amazon, Facebook, Microsoft, Salesforce, and Etsy. His work has been selected for several awards, including an NSF CAREER award, and faculty awards from Amazon, Salesforce, Facebook, and Qualcomm, among others.
Inhaltsangabe
1. Introduction Part I. Machine Learning Primer: 2. Regression and feature engineering 3. Classification and the learning pipeline Part II. Fundamentals of Personalized Machine Learning: 4. Introduction to recommender systems 5. Model-based approaches to recommendation 6. Content and structure in recommender systems 7. Temporal and sequential models Part III. Emerging Directions in Personalized Machine Learning: 8. Personalized models of text 9. Personalized models of visual data 10. The consequences of personalized machine learning References Index.
1. Introduction Part I. Machine Learning Primer: 2. Regression and feature engineering 3. Classification and the learning pipeline Part II. Fundamentals of Personalized Machine Learning: 4. Introduction to recommender systems 5. Model-based approaches to recommendation 6. Content and structure in recommender systems 7. Temporal and sequential models Part III. Emerging Directions in Personalized Machine Learning: 8. Personalized models of text 9. Personalized models of visual data 10. The consequences of personalized machine learning References Index.
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