000			nam a22 7a 4500
999			_c29379 _d29379
008			190311b xxu||||| |||| 00| 0 eng d
020			_a9781107154889 _c(hbk)
082			_a518.1 _bMIT
100			_aMitzenmacher, Michael
245			_aProbability and computing : randomization and probabilistic techniques in algorithms and data analysis
250			_a2nd ed.
260			_aCambridge : _bCambridge University Press, _c2017
300			_axx, 467 p. : _bill. ; _c26 cm.
365			_aGBP _b44.99 _d00
504			_aIncludes bibliographical references and index.
520			_aGreatly 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			_aAlgorithms
650			_aProbabilities
650			_aStochastic analysis
650			_aComputer science
650			_aMathematics
700			_aUpfal, Eli
942			_2ddc _cBK