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.

Exercises and examples

Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems.

Intended audience

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.