Probability and computing : Randomization and probabilistic techniques in algorithms and data analysis/ Michael Mitzenmacher, Eli Upfal.
Publisher: New York: Cambridge University Press, 2017Edition: 2nd edDescription: 467 pISBN: 9781107154889Subject(s): Algorithms | Probabilities | Stochastic analysisDDC classification: 518.1 Online resources: Click here to access online | Click here to access online | Click here to access online 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"--| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Mahatma Gandhi University Library General Stacks | 518.1 Q7 (Browse shelf) | Available | 59431 |
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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