Format:
1 Online-Ressource (x, 326 Seiten)
,
Diagramme
ISBN:
9781009003971
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 24 Jan 2022)
Additional Edition:
ISBN 9781316518908
Additional Edition:
Erscheint auch als Druck-Ausgabe McAuley, Julian Personalized machine learning Cambridge : Cambridge University Press, 2022 ISBN 9781316518908
Language:
English
Keywords:
Maschinelles Lernen
;
Data Mining
;
Klassifikation
DOI:
10.1017/9781009003971
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