Probability and Computing
Randomization and Probabilistic Techniques in Algorithms and Data Analysis
(Kindle Edition)

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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.

AuthorMichael Mitzenmacher
BindingKindle Edition
FormatKindle eBook
Language TypePublished
Number Of Pages489
Product GroupeBooks
Publication Date2017-03-31
PublisherCambridge University Press
Release Date2017-06-20
StudioCambridge University Press
Sales Rank221092

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