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dc.contributor.authorWalrand, Jean
dc.description.abstractThis 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 This is an open access book.
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJK Communications engineering / telecommunications
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBJ Maths for engineers
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PB Mathematics::PBT Probability & statistics
dc.subject.otherProbability and Statistics in Computer Science
dc.subject.otherCommunications Engineering, Networks
dc.subject.otherMathematical and Computational Engineering
dc.subject.otherProbability Theory and Stochastic Processes
dc.subject.otherStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
dc.subject.otherMathematical and Computational Engineering Applications
dc.subject.otherProbability Theory
dc.subject.otherStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
dc.subject.otherApplied probability
dc.subject.otherHypothesis testing
dc.subject.otherDetection theory
dc.subject.otherExpectation maximization
dc.subject.otherStochastic dynamic programming
dc.subject.otherMachine learning
dc.subject.otherStochastic gradient descent
dc.subject.otherDeep neural networks
dc.subject.otherMatrix completion
dc.subject.otherLinear and polynomial regression
dc.subject.otherOpen Access
dc.subject.otherMaths for computer scientists
dc.subject.otherMathematical & statistical software
dc.subject.otherCommunications engineering / telecommunications
dc.subject.otherMaths for engineers
dc.subject.otherProbability & statistics
dc.titleProbability in Electrical Engineering and Computer Science
dc.title.alternativeAn Application-Driven Course
oapen.imprintSpringer International Publishing
oapen.grant.number[grantnumber unknown]

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open access
Except where otherwise noted, this item's license is described as open access