000			02613cam a22003138i 4500
001			22166249
003			KWUST
005			20230824133527.0
008			210805s2021 enk b 001 0 eng
010			_a 2021038652
020			_a9781108940023 _q(paperback)
040			_aDLC _beng _erda _cKWUST
042			_apcc
050	0	0	_aQ325.5.J53 2021
100	1		_aJiang, Hui _c(Computer scientist), _eauthor.
245	1	0	_aMachine learning fundamentals : _ba concise introduction / _cHui Jiang, York University, Toronto.
264		1	_aUnited Kingdom ; _aNew York, NY : _bCambridge University Press, _c2021.
300			_axviii,380p.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a"This 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. Hui Jiang is Professor of Electrical Engineering and Computer Science at York University, where he has been since 2002. His main research interests include machine learning, particularly deep learning, and its applications to speech and audio processing, natural language processing, and computer vision. Over the past 30 years, he has worked on a wide range of research problems from these areas and published hundreds of technical articles and papers in the mainstream journals and top-tier conferences. His works have won the prestigious IEEE Best Paper Award and the ACL Outstanding Paper honor"-- _cProvided by publisher.
650		0	_aMachine learning.
650		7	_aCOMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition _2bisacsh
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c2478 _d2478