LEADER 03921nam 22007813u 450 001 996464521903316 005 20230221125024.0 010 $a3-030-49995-2 035 $a(CKB)5590000000516194 035 $aEBL6647753 035 $a(OCoLC)1319210231 035 $a(AU-PeEL)EBL6647753 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/71297 035 $a(PPN)260307084 035 $a(EXLCZ)995590000000516194 100 $a20220617d2021|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProbability in Electrical Engineering and Computer Science$b[electronic resource] $eAn Application-Driven Course 210 $aCham $cSpringer International Publishing AG$d2021 215 $a1 online resource (390 p.) 300 $aDescription based upon print version of record. 311 $a3-030-49994-4 330 $aThis 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. 606 $aMaths for computer scientists$2bicssc 606 $aCommunications engineering / telecommunications$2bicssc 606 $aMaths for engineers$2bicssc 606 $aProbability & statistics$2bicssc 610 $aProbability and Statistics in Computer Science 610 $aCommunications Engineering, Networks 610 $aMathematical and Computational Engineering 610 $aProbability Theory and Stochastic Processes 610 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences 610 $aMathematical and Computational Engineering Applications 610 $aProbability Theory 610 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 610 $aApplied probability 610 $aHypothesis testing 610 $aDetection theory 610 $aExpectation maximization 610 $aStochastic dynamic programming 610 $aMachine learning 610 $aStochastic gradient descent 610 $aDeep neural networks 610 $aMatrix completion 610 $aLinear and polynomial regression 610 $aOpen Access 610 $aMaths for computer scientists 610 $aMathematical & statistical software 610 $aCommunications engineering / telecommunications 610 $aMaths for engineers 610 $aProbability & statistics 610 $aStochastics 615 7$aMaths for computer scientists 615 7$aCommunications engineering / telecommunications 615 7$aMaths for engineers 615 7$aProbability & statistics 700 $aWalrand$b Jean$0103675 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a996464521903316 996 $aProbability in Electrical Engineering and Computer Science$92870258 997 $aUNISA