Personalized Machine Learning
Every day we interact with machine learning systems offering individualized predic-
tions for our entertainment, social connections, purchases, or health. These involve
several modalities of data, from sequences of clicks to text, images, and social inter-
actions. 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
how to adapt them to settings involving user data; then presents techniques based on
advanced principles such as matrix factorization, deep learning, and generative mod-
eling; 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.
julian mcauleyhas been a Professor at the University of California San Diego
since 2014. Personalized Machine Learning is the main research area of his lab,
with applications ranging from personalized recommendation to dialog, health care,
and fashion design. He regularly collaborates with industry on these topics, including
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.
Personalized Machine Learning
JULIAN MCAULEY
University of California San Diego
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Personalized Machine Learning_Julian McAuley.pdf