000			02038nam a2200337 i 4500
001			CR9781108938051
003			UkCbUP
005			20240912192320.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			200508s2021||||enk o ||1 0|eng|d
020			_a9781108938051 (ebook)
020			_z9781108837040 (hardback)
020			_z9781108940023 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ325.5 _b.J53 2021
082	0	0	_a006.3/1 _223
100	1		_aJiang, Hui _c(Computer scientist), _eauthor.
245	1	0	_aMachine learning fundamentals : _ba concise introduction / _cHui Jiang, York University, Toronto.
264		1	_aCambridge : _bCambridge University Press, _c2021.
300			_a1 online resource (xviii, 380 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 26 Nov 2021).
520			_aThis lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.
650		0	_aMachine learning.
776	0	8	_iPrint version: _z9781108837040
856	4	0	_uhttps://doi.org/10.1017/9781108938051
942			_2ddc _cEB
999			_c9475 _d9475