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Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis

Upfal Mitzenmacher

€ 71.05

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Description for Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis hardcover. .
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 ... Read more

Product Details

Publisher

Cambridge University Press

Format

Hardback

Publication date

2017

Condition

New

Number of Pages

484

Place of Publication

Cambridge, United Kingdom

ISBN

9781107154889

SKU

V9781107154889

Shipping Time

Usually ships in 4 to 8 working days

Ref

99-2

About Upfal Mitzenmacher

Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University, Massachusetts. Professor Mitzenmacher has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. His work on low-density parity-check codes ... Read more

Reviews for Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis

'As randomized methods continue to grow in importance, this textbook provides a rigorous yet accessible introduction to fundamental concepts that need to be widely known. The new chapters in this second edition, about sample size and power laws, make it especially valuable for today's applications.' Donald E. Knuth, Stanford University, California 'Of all the courses I have taught at Berkeley, ... Read moremy favorite is the one based on the Mitzenmacher-Upfal book Probability and Computing. Students appreciate the clarity and crispness of the arguments and the relevance of the material to the study of algorithms. The new second edition adds much important material on continuous random variables, entropy, randomness and information, advanced data structures and topics of current interest related to machine learning and the analysis of large data sets.' Richard M. Karp, University of California, Berkeley 'The new edition is great. I'm especially excited that the authors have added sections on the normal distribution, learning theory and power laws. This is just what the doctor ordered or, more precisely, what teachers such as myself ordered!' Anna Karlin, University of Washington 'By assuming just an elementary introduction to discrete probability and some mathematical maturity, this book does an excellent job of introducing a great variety of topics to the reader. I especially liked the coverage of the Poisson, exponential, and (multi-variate) normal distributions and how they arise naturally, machine learning, Bayesian reasoning, Cuckoo hashing etc. There is a broad range of exercises, including helpful ones on programming to get a feel for the numerics ... This connection to practice is unusual and very commendable ... Overall, I would highly recommend this book to anyone interested in probabilistic and statistical foundations as applied to computer science, data science, etc. It can be taught at the senior undergraduate or graduate level to students in computer science, electrical engineering, operations research, mathematics, and other such disciplines.' Frederic Green , SIGACT News 'As randomized methods continue to grow in importance, this textbook provides a rigorous yet accessible introduction to fundamental concepts that need to be widely known. The new chapters in this second edition, about sample size and power laws, make it especially valuable for today's applications.' Donald E. Knuth, Stanford University 'Of all the courses I have taught at Berkeley, my favorite is the one based on the Mitzenmacher-Upfal book Probability and Computing. Students appreciate the clarity and crispness of the arguments and the relevance of the material to the study of algorithms. The new Second Edition adds much important material on continuous random variables, entropy, randomness and information, advanced data structures and topics of current interest related to machine learning and the analysis of large data sets.' Richard M. Karp, University of California, Berkeley 'The new edition is great. I'm especially excited that the authors have added sections on the normal distribution, learning theory and power laws. This is just what the doctor ordered or, more precisely, what teachers such as myself ordered!' Anna Karlin, University of Washington Show Less

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Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis

Upfal Mitzenmacher

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