Lecture notes for the Yale Computer Science course CPSC 4690/5690 Randomized Algorithms. Suitable for use as a supplementary text for an introductory graduate or advanced undergraduate course on randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, applications of martingales and Markov chains, and the Lovász Local Lemma. Algorithmic topics include analysis of classic randomized algorithms such as Quicksort and Hoare's FIND, randomized tree data structures, hashing, Markov chain Monte Carlo sampling, randomized approximate counting, derandomization, quantum computing, and some examples of randomized distributed algorithms.

翻译：耶鲁大学计算机科学课程CPSC 4690/5690《随机化算法》的授课讲义。本讲义适用于研究生入门或高年级本科生随机化算法课程作为补充教材。内容涵盖概率论工具，包括随机变量与期望、并集界论证、集中界、鞅与马尔可夫链的应用，以及洛瓦兹局部引理。算法主题包括经典随机化算法分析（如快速排序与Hoare的FIND算法）、随机化树数据结构、哈希技术、马尔可夫链蒙特卡洛采样、随机化近似计数、去随机化、量子计算，以及若干随机化分布式算法实例。