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oapen-20.500.12657-500162021-07-15T00:57:46Z Probability in Electrical Engineering and Computer Science Walrand, Jean 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 bic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJK Communications engineering / telecommunications bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBJ Maths for engineers bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics 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. 2021-07-14T09:58:07Z 2021-07-14T09:58:07Z 2021 book ONIX_20210714_9783030499952_7 9783030499952 https://library.oapen.org/handle/20.500.12657/50016 eng application/pdf n/a 978-3-030-49995-2.pdf https://www.springer.com/9783030499952 Springer Nature Springer International Publishing 10.1007/978-3-030-49995-2 10.1007/978-3-030-49995-2 6c6992af-b843-4f46-859c-f6e9998e40d5 1edf7275-d11e-45ad-a038-9270d4ffa2e0 9783030499952 Springer International Publishing 380 [grantnumber unknown] University of California, Berkeley Foundation UC Berkeley Foundation open access
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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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