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101 0 $aeng
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200 10$aLinear algebra and probability for computer science applications /$fErnest Davis
210   $aBoca Raton, FL $cCRC Press$d©2012
215   $a1 online resource (430 p.)
225 0 $aAn A K Peters Book
300   $aDescription based upon print version of record.
311   $a1-4665-0155-3 
320   $aIncludes bibliographical references.
327   $aFront Cover; Dedication; Contents; Preface; 1. MATLAB; I: Linear Algebra; 2. Vectors; 3. Matrices; 4. Vector Spaces; 5. Algorithms; 6. Geometry; 7. Change of Basis, DFT, and SVD; II: Probability; 8. Probability; 9. Numerical Random Variables; 10. Markov Models; 11. Confidence Intervals; 12. Monte Carlo Methods; 13. Information and Entropy; 14. Maximum Likelihood Estimation; References; Notation
330   $a"Taking a computer scientist's point of view, this classroom-tested text gives an introduction to linear algebra and probability theory, including some basic aspects of statistics. It discusses examples of applications from a wide range of areas of computer science, including computer graphics, computer vision, robotics, natural language processing, web search, machine learning, statistical analysis, game playing, graph theory, scientific computing, decision theory, coding, cryptography, network analysis, data compression, and signal processing. It includes an extensive discussion of MATLAB, and includes numerous MATLAB exercises and programming assignments"--$cProvided by publisher.
606   $aComputer science$xMathematics
606   $aAlgebras, Linear
606   $aProbabilities
615  0$aComputer science$xMathematics.
615  0$aAlgebras, Linear.
615  0$aProbabilities.
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