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

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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Walrand, Jean.
Year of Publication:2021
Language:English
Physical Description:1 online resource (390 p.)
Notes:Description based upon print version of record.
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spelling Walrand, Jean.
Probability in Electrical Engineering and Computer Science : An Application-Driven Course
Cham : Springer International Publishing AG, 2021.
1 online resource (390 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Description based upon print version of record.
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.
English
University of California, Berkeley Foundation
Maths for computer scientists bicssc
Communications engineering / telecommunications bicssc
Maths for engineers bicssc
Probability & statistics bicssc
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
Maths for computer scientists
Mathematical & statistical software
Communications engineering / telecommunications
Maths for engineers
Probability & statistics
Stochastics
3-030-49994-4
language English
format eBook
author Walrand, Jean.
spellingShingle Walrand, Jean.
Probability in Electrical Engineering and Computer Science : An Application-Driven Course
author_facet Walrand, Jean.
author_variant j w jw
author_sort Walrand, Jean.
title Probability in Electrical Engineering and Computer Science : An Application-Driven Course
title_sub An Application-Driven Course
title_full Probability in Electrical Engineering and Computer Science : An Application-Driven Course
title_fullStr Probability in Electrical Engineering and Computer Science : An Application-Driven Course
title_full_unstemmed Probability in Electrical Engineering and Computer Science : An Application-Driven Course
title_auth Probability in Electrical Engineering and Computer Science : An Application-Driven Course
title_new Probability in Electrical Engineering and Computer Science :
title_sort probability in electrical engineering and computer science : an application-driven course
publisher Springer International Publishing AG,
publishDate 2021
physical 1 online resource (390 p.)
isbn 3-030-49995-2
3-030-49994-4
callnumber-first Q - Science
callnumber-subject QA - Mathematics
callnumber-label QA76
callnumber-sort QA 276.9 M35
illustrated Not Illustrated
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status_str n
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(MiAaPQ)EBC6647753
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carrierType_str_mv cr
is_hierarchy_title Probability in Electrical Engineering and Computer Science : An Application-Driven Course
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