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Probability and computing / Michael Mitzenmacher ;Eli Upfal.

By: Contributor(s): Material type: TextTextPublisher: Cambridge, United Kingdom ; New York, NY, USA : Cambridge University Press, [2017]Edition: Second editionDescription: pages cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781107154889
Subject(s): DDC classification:
  • 519.2 M679p 22
LOC classification:
  • QA274 .M574 2017
Online resources: Summary: "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.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Chittagong University Library 519.2 M679p (Browse shelf(Opens below)) c-1 Available 329020
Books Books Chittagong University Library 519.2 M679p (Browse shelf(Opens below)) C-2 Available 329021
Books Books Chittagong University Library 519.2 M679p (Browse shelf(Opens below)) C-3 Available 329022
Books Books Chittagong University Library 519.2 M679p (Browse shelf(Opens below)) C-4 Available 329023
Books Books Chittagong University Library 519.2 M679p (Browse shelf(Opens below)) C-5 Available 329024

Includes bibliographical references and index.

"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.

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