Electronic Resource

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

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Penilaian

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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. 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.

This is an open access book.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2008000023	EB0003771	519.2 Wal p	Central Library (Database Springer E-Book)	Available

Detail Information

Series Title

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Call Number

519.2 Wal p

Publisher

Switzerland : ., 2021

Collation

xxi, 380p.: Ill

Language

English

ISBN/ISSN

978-3-030-49995-2

Classification

519.2

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Computer science--Mathematics
Electrical engineering--Mathematics

Specific Detail Info

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Statement of Responsibility

RTS

Other version/related

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