Probability and computing : Randomization and probabilistic techniques in algorithms and data analysis/ Michael Mitzenmacher, Eli Upfal.

By:

Mitzenmacher, Michael

Contributor(s):

Upfal, Eli

Publication details: New York: Cambridge University Press, 2017.Edition: 2nd edDescription: 467 pISBN:

9781107154889

Subject(s):

DDC classification:

518.1 Q7

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"--

List(s) this item appears in: New Additions March-April 2019

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Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	Mahatma Gandhi University Library General Stacks	518.1 Q7 (Browse shelf(Opens below))	Available		59431

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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"--

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