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020 _a9781107298019
_qelectronic book
039 9 _a201606081045
_basmany
_c201605261139
_dbaiti
_y10-16-2015
_zhafiz
_wUPO_10044530-hafizupload16102015.mrc
_x63
040 _aUkCbUP
_cUkCbUP
_erda
050 0 0 _aebookQ325.5
_b.S475 2014
090 _aebookQ325.5.S475 2014
100 1 _aShalev-Shwartz, Shai,
_eauthor.
245 1 0 _aUnderstanding Machine Learning :
_bFrom Theory to Algorithms /
_cShai Shalev-Shwartz, Shai Ben-David.
264 1 _aCambridge :
_bCambridge University Press,
_c2014.
300 _a1 online resource (409 pages) :
_bdigital, PDF file(s).
336 _atext
_2rdacontent
337 _acomputer
_2rdamedia
338 _aonline resource
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 08 Oct 2015).
520 _aMachine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
650 0 _aMachine learning
650 0 _aAlgorithms
700 1 _aBen-David, Shai,
_eauthor.
773 0 _tCambridge Books Online
776 0 8 _iPrint version:
_z9781107057135
856 4 0 _uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=http://dx.doi.org/10.1017/CBO9781107298019
907 _a.b16220031
_b2022-10-14
_c2019-11-12
942 _c01
_n0
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998 _ae
_b2015-03-10
_cm
_dz
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_genk
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_z.b16220031
999 _c599706
_d599706