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dc.contributor.authorWalrand, Jean
dc.date.accessioned2021-07-14T09:58:07Z
dc.date.available2021-07-14T09:58:07Z
dc.date.issued2021
dc.identifierONIX_20210714_9783030499952_7
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/50016
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 mary.james@springer.com. This is an open access book.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientistsen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunicationsen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBJ Maths for engineersen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statisticsen_US
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.subject.otherStochastics*
dc.subject.otherTextbook*
dc.titleProbability in Electrical Engineering and Computer Science
dc.title.alternativeAn Application-Driven Course
dc.typebook
oapen.identifier.doi10.1007/978-3-030-49995-2
oapen.relation.isPublishedBy6c6992af-b843-4f46-859c-f6e9998e40d5
oapen.relation.isFundedBy1edf7275-d11e-45ad-a038-9270d4ffa2e0
oapen.relation.isbn9783030499952
oapen.imprintSpringer International Publishing
oapen.pages380
oapen.grant.number[grantnumber unknown]


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