The book is a good resource for scientists, researchers, and practitioners who have a basic understanding of algorithms, control, and data mining in intelligent systems. It requires some background in these areas in order to grasp the material. Each chapter includes definitions and theorems on the topic. The information is also supported by related literature. There is a rich list of references at the end of the book, and the appendix provides Internet links and explanations of synthetic and real-life datasets so that readers can experiment.

The book has seven chapters. Part 1 has two chapters. The first chapter explains feedback, averaging, and randomization in control and data mining. Chapter 2 gives a historical overview of game theory, Monte Carlo methods, linear regression, and data mining. Part 2 has three chapters about randomization in estimation, identification, and filtering problems under arbitrary external noises. Part 3 consists of two chapters on clustering and cluster validation.

This book is for people from disciplines such as computer science, engineering, and mathematics, who plan to work on randomized algorithms in intelligent systems. It is written in a scientific way, hence it can also be used as a graduate-level textbook.