Electronic Resource

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

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. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com.

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Detail Information

Series Title

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Call Number

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Publisher

: Springer Cham., 2021

Collation

XXI, 380 hlm; ill., lamp.,

Language

English

ISBN/ISSN

9783030499952

Classification

-

Content Type

text

Media Type

computer

Carrier Type

other (computer)

Edition

1

Subject(s)

Communications Engineering, Networks
Chemistry and Earth Sciences
Mathematical and Computational Engineering
Computer Science,

Probability Theory and Stochastic Processes,

Specific Detail Info

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 This book is open access, which means that you have free and unlimited access

Statement of Responsibility

Jean Walrand

Other Information

Cataloger

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Source

https://link.springer.com/book/10.1007/978-3-030-49995-2

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-3-030-49995-2

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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