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

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This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. The companion website now has many examples of Python demos and also Python labs used in Berkeley.

Showcases techniques of applied probability with applications in EE and CS;
Presents all topics with concrete applications so students see the relevance of the theory;
Illustrates methods with Jupyter notebooks that use widgets to enable the users to modify parameters.

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Detail Information

Series Title

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Call Number

519.2 PRO J

Publisher

Cham, Switzerland : Springer Cham., 2021

Collation

xxi, 380 p.

Language

English

ISBN/ISSN

9783030499952

Classification

519.2

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

physics
Communications Engineering
Computer Science
networks
Probability and Statistics in Computer Science
Probability Theory
Statistics in Engineering
Chemistry and Earth Sciences

Specific Detail Info

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Statement of Responsibility

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