Description
| Product ID: | 9781107154889 |
| Product Form: | Hardback |
| Country of Manufacture: | US |
| Title: | Probability and Computing |
| Subtitle: | Randomization and Probabilistic Techniques in Algorithms and Data Analysis |
| Authors: | Author: Eli Upfal, Michael Mitzenmacher |
| Page Count: | 484 |
| Subjects: | Probability and statistics, Probability & statistics, Algorithms and data structures, Algorithms & data structures |
| Description: | Select Guide Rating This greatly expanded new edition, requiring only an elementary background in discrete mathematics, comprehensively covers randomization and probabilistic techniques in modern computer science. It includes new material relevant to machine learning and big data analysis, plus examples and exercises, enabling students to learn modern techniques and applications. 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. |
| Imprint Name: | Cambridge University Press |
| Publisher Name: | Cambridge University Press |
| Country of Publication: | GB |
| Publishing Date: | 2017-07-03 |
