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

By:

Walrand, Jean [auth]

Material type: Article

ArticleLanguage: English Publication details: Springer Nature 2021Description: 1 electronic resource (380 p.) Carrier type:

online resource

ISBN:

9783030499952
9783030499952

Subject(s):

Online resources:

Summary: 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.

List(s) this item appears in: Faculty Informational Technology

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Holdings
Item type	Current library	Collection	Shelving location	Call number	Status	Notes	Date due	Barcode
Electronic edition	Bucheon University Library	Computers	DOAB	004.4 P93	Not for loan	Download (pdf)		1010574

Open Access star Unrestricted online access

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.