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Personalized Machine Learning

by Julian McAuley
Save 17% Save 17%
Current price ₹5,538.00
Original price ₹6,646.00
Original price ₹6,646.00
Original price ₹6,646.00
(-17%)
₹5,538.00
Current price ₹5,538.00

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Book cover type: Hardcover
  • ISBN13: 9781316518908
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: Cambridge University Press
  • Publisher Imprint: Cambridge University Press
  • Publication Date:
  • Pages: 338
  • Original Price: USD 56.0
  • Language: English
  • Edition: N/A
  • Item Weight: 622 grams
  • BISAC Subject(s): Database Administration & Management

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.

McAuley, Julian: - 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.

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