Probability in Electrical Engineering and Computer Science: An Application-Driven Course

This textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). It presents information processing and communication systems that use probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. Python labs enable readers to experiment and consolidate their understanding.

Format: Paperback / softback
Length: 380 pages
Publication date: 23 June 2022
Publisher: Springer Nature Switzerland AG

This revised textbook is a comprehensive guide that aims to motivate and illustrate the techniques of applied probability by showcasing their applications in electrical engineering and computer science (EECS). The author presents a wide range of information processing and communication systems that utilize algorithms based on probabilistic models and techniques, such as web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation.

In order to facilitate a deeper understanding of these applications, the author proceeds to explain how they operate, while also providing readers with a solid foundation in the key concepts and methods of applied probability. To enhance the learning experience, Python labs are included, allowing readers to experiment and consolidate their knowledge.

The book is organized into chapters, each covering a specific topic within applied probability. It begins with an introduction to probability theory and its applications, followed by chapters on random variables, probability distributions, and conditional probability. Subsequent chapters explore Markov chains, stochastic processes, and optimization techniques, which are fundamental in understanding and solving complex problems in EECS.

New topics have been added to this edition, including Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. These topics reflect the latest developments in the field and provide readers with the tools they need to address emerging challenges in EECS.

In addition to the theoretical content, the book includes homework problems, solutions, and Jupyter notebooks. These resources are designed to assist readers in applying the concepts learned in the textbook to real-world scenarios.

This edition of the textbook is open access, making it freely available to anyone with an internet connection. It is a valuable resource for students, researchers, and practitioners in the fields of electrical engineering, computer science, and related disciplines.

If you have any ancillaries related to this book, such as examples of Python demos or Python labs used in Berkeley, please email Mary James at mary.james@springer.com.

Weight: 617g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030499976
Edition number: 1st ed. 2021