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Probability and computing

Mitzenmacher, Michael (1969-)

Probability and computing

紀錄類型:	書目-語言資料,印刷品 : 單行本
作者:	MitzenmacherMichael, 1969-
其他作者:	UpfalEli, 1954-
出版地:	Cambridge, UK
出版者:	Cambridge University Press;
出版年:	c2017.
版本:	2nd ed.
面頁冊數:	xx, 467 p.ill. : 26 cm.;
標題:	Algorithms. -
標題:	Probabilities. -
標題:	Stochastic analysis. -
摘要註:	"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"--Provided by publisher.
ISBN:	9781107154889

Probability and computing
Mitzenmacher, Michael

Probability and computing / Michael Mitzenmacher Eli Upfal. - 2nd ed.. - Cambridge, UK : Cambridge University Press, c2017.. - xx, 467 p. ; ill. ; 26 cm..
Includes bibliographical references and index..
ISBN 9781107154889ISBN 110715488X
Algorithms.Probabilities.Stochastic analysis.

Upfal, Eli

Probability and computing
LDR:01516cam a22002052 450 001 390432
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210 $a Cambridge, UK $a New York, NY, USA $c Cambridge University Press $d c2017.
215 1 $a xx, 467 p. $c ill. $d 26 cm.
320 $a Includes bibliographical references and index.
330 $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"--Provided by publisher.
606 $a Algorithms. $2 lc $3 26649
606 $a Probabilities. $2 lc $3 28174
606 $a Stochastic analysis. $2 lc $3 381912
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700 1 $a Mitzenmacher $b Michael $f 1969- $3 381910
702 1 $a Upfal $b Eli $f 1954- $3 381911
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