000			02277nam a22003737a 4500
001			19283649
003			IIITD
005			20220910110719.0
008			160921s2017 enk b 001 0 eng
010			_a 2016041654
020			_a9781107154889
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aQA274 _b.M574 2017
082	0	0	_a518.1 _bMIT-P
100	1		_aMitzenmacher, Michael.
245	1	0	_aProbability and computing : _brandomization and probabilistic techniques in algorithms and data analysis _cby Michael Mitzenmacher and Eli Upfal
250			_a2nd ed.
260			_aNew Delhi : _bCambridge University Press, _c©2017
263			_a1704
300			_axx, 467 p. ; _c26 cm.
500			_aThis book includes bibliographical references and index.
520			_a"Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics"--
650		0	_aAlgorithms.
650		0	_aProbabilities.
650		0	_aStochastic analysis.
700	1		_aUpfal, Eli
856	4	2	_3Contributor biographical information _uhttps://www.loc.gov/catdir/enhancements/fy1618/2016041654-b.html
856	4	2	_3Publisher description _uhttps://www.loc.gov/catdir/enhancements/fy1618/2016041654-d.html
856	4	1	_3Table of contents only _uhttps://www.loc.gov/catdir/enhancements/fy1618/2016041654-t.html
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c157424 _d157424