國立虎尾科技大學 |

Personalized machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Personalized machine learning // Julian McAuley.
Author:	McAuley, Julian,
Description:	1 online resource (x, 326 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 24 Jan 2022).
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1017/9781009003971
ISBN:	9781009003971 (ebook)

Personalized machine learning /
McAuley, Julian,

Personalized machine learning /Julian McAuley. - 1 online resource (x, 326 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 24 Jan 2022).

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.

ISBN: 9781009003971 (ebook)Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .M386 2022

Dewey Class. No.: 006.31

Personalized machine learning /
LDR:02009nam a2200277 i 4500 001 1126088
003 UkCbUP
005 20220204062405.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2022||||enk o ||1 0|eng|d
020 $a 9781009003971 (ebook)
020 $z 9781316518908 (hardback)
035 $a CR9781009003971
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a Q325.5 $b .M386 2022
082 0 4 $a 006.31 $2 23
100 1 $a McAuley, Julian, $e author. $3 1444706
245 1 0 $a Personalized machine learning / $c Julian McAuley.
264 1 $a Cambridge, United Kingdom ; New York, NY : $b Cambridge University Press, $c 2022.
300 $a 1 online resource (x, 326 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 24 Jan 2022).
520 $a 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.
650 0 $a Machine learning. $3 561253
776 0 8 $i Print version: $z 9781316518908
856 4 0 $u https://doi.org/10.1017/9781009003971