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|All Authors / Contributors:||
|Description:||xviii, 413 pages : illustrations ; 25 cm|
Vector spaces --
Change of basis, DFT, and SVD --
Numerical random variables --
Markov models --
Confidence intervals --
Monte Carlo methods --
Information and entropy --
Maximum likelihood estimation.
"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"--
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