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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, includin...

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Bibliographic Details
Main Author: Walrand, Jean (auth)
Format: Electronic Book Chapter
Language:English
Published: Springer Nature 2021
Subjects:
Maths for computer scientists
Communications engineering / telecommunications
Maths for engineers
Probability & statistics
Probability and Statistics in Computer Science
Communications Engineering, Networks
Mathematical and Computational Engineering
Probability Theory and Stochastic Processes
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Mathematical and Computational Engineering Applications
Probability Theory
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Applied probability
Hypothesis testing
Detection theory
Expectation maximization
Stochastic dynamic programming
Machine learning
Stochastic gradient descent
Deep neural networks
Matrix completion
Linear and polynomial regression
Open Access
Mathematical & statistical software
Stochastics
Online Access:OAPEN Library: download the publication
OAPEN Library: description of the publication
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Description
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.
Physical Description:1 electronic resource (380 p.)
ISBN:978-3-030-49995-2
9783030499952
Access:Open Access

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