Personalized machine learning:

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...

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Bibliographic Details
Main Author: McAuley, Julian ca. 20./21. Jh (Author)
Format: Electronic eBook
Language:English
Published: Cambridge, United Kingdom ; New York, NY Cambridge University Press 2022
Subjects:
Online Access:BSB01
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Summary: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.
Physical Description:1 Online-Ressource (x, 326 Seiten)
ISBN:9781009003971
DOI:10.1017/9781009003971

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