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Probability and Computing: Randomization and Probabilistic Techniques in
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Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis Hardback - 2017 - 2nd Edition

by Michael Mitzenmacher; Eli Upfal

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From the publisher

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.

Details

  • Title Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis
  • Author Michael Mitzenmacher; Eli Upfal
  • Binding Hardback
  • Edition number 2nd
  • Edition 2
  • Pages 484
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2017-07-03
  • Features Bibliography, Index
  • ISBN 9781107154889 / 110715488X
  • Weight 2.45 lbs (1.11 kg)
  • Dimensions 10 x 7.1 x 1.1 in (25.40 x 18.03 x 2.79 cm)
  • Category Computers - Languages / Programming
  • Library of Congress subjects Algorithms, Probabilities
  • Library of Congress Catalogue Number 2016041654
  • Dewey Decimal Code 518.1
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ISBN: 9781107154889
Cambridge University Press, 01 January 2017
Hardback, 467 pages
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.
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