000			02132nam a2200325 i 4500
001			CR9781009003971
003			UkCbUP
005			20240913192936.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			201022s2022||||enk o ||1 0|eng|d
020			_a9781009003971 (ebook)
020			_z9781316518908 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.5 _b.M386 2022
082	0	4	_a006.31 _223
100	1		_aMcAuley, Julian, _eauthor.
245	1	0	_aPersonalized machine learning / _cJulian McAuley.
264		1	_aCambridge, United Kingdom ; New York, NY : _bCambridge University Press, _c2022.
300			_a1 online resource (x, 326 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 24 Jan 2022).
520			_aEvery 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	_aMachine learning.
776	0	8	_iPrint version: _z9781316518908
856	4	0	_uhttps://doi.org/10.1017/9781009003971
942			_2ddc _cEB
999			_c9828 _d9828