03932nam 22007933u 450 991048870900332120230912163632.03-030-49995-2(CKB)5590000000516194EBL6647753(OCoLC)1319210231(AU-PeEL)EBL6647753(oapen)https://directory.doabooks.org/handle/20.500.12854/71297(MiAaPQ)EBC6647753(PPN)260307084(EXLCZ)99559000000051619420220617d2021|||| u|| |engur|n|---|||||txtrdacontentcrdamediacrrdacarrierProbability in Electrical Engineering and Computer Science An Application-Driven CourseCham Springer International Publishing AG20211 online resource (390 p.)Description based upon print version of record.3-030-49994-4 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.Maths for computer scientistsbicsscCommunications engineering / telecommunicationsbicsscMaths for engineersbicsscProbability & statisticsbicsscProbability and Statistics in Computer ScienceCommunications Engineering, NetworksMathematical and Computational EngineeringProbability Theory and Stochastic ProcessesStatistics for Engineering, Physics, Computer Science, Chemistry and Earth SciencesMathematical and Computational Engineering ApplicationsProbability TheoryStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesApplied probabilityHypothesis testingDetection theoryExpectation maximizationStochastic dynamic programmingMachine learningStochastic gradient descentDeep neural networksMatrix completionLinear and polynomial regressionOpen AccessMaths for computer scientistsMathematical & statistical softwareCommunications engineering / telecommunicationsMaths for engineersProbability & statisticsStochasticsMaths for computer scientistsCommunications engineering / telecommunicationsMaths for engineersProbability & statisticsWalrand Jean103675AU-PeELAU-PeELAU-PeELBOOK9910488709003321Probability in Electrical Engineering and Computer Science2870258UNINA