COURSE DETAIL
This course discusses different concepts of numerical analysis including algorithms, stability, accuracy, and efficiency. Topics include: errors, algorithms, and estimates; nonlinear equations and nonlinear systems; methods for linear systems of equations; polynomial interpolation-- Lagrange, Hermite, piecewise, and splines; numerical quadrature and differentiation.
Prerequisites: Linear Algebra, Differential Calculus, Integral Calculus, and Programming.
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
Topics cover include: Bivariate probability, continuous densities, generating functions. The exponential densities, including normal, t-, χ2 and F. Simple parametric and nonparametric tests. Further topics include the consistency, efficiency and sufficiency of estimates, maximum likelihood estimation; the central limit theorem, Chebyshev's inequality, the Neyman-Pearson lemma and the likelihood ratio test; regression, and analysis of variance.
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
This course provides the analytic background behind quantum information theory in the framework of operators on Hilbert spaces and functional analysis. Topics include completely positive and completely bounded maps; operator systems and spaces; Choi representation and Kraus operators; Stinespring's representation theorem; tensor products; quantum measurements and related sets of correlations; entanglement; Schmidt decompositions; and factorizable channels and applications in quantum information theory.
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