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Signal Theory

EESC 6350

Erik Jonsson School of Engineering and Computer Science

Signal processing applications and signal spaces, vector spaces, matrix inverses and orthogonal projections, four fundamental subspaces, least squares and minimum norm solutions, the SVD and principal component analysis, subspace approximation, infinite dimensional spaces, linear operators, norms, inner products and Hilbert spaces, projection theorems, spectral properties of Hermitian operators, Hilbert spaces of random variables, linear minimum variance estimation and the Levinson-Durbin algorithm, general optimization over Hilbert spaces, methods and applications of optimization. 3 credit hours.

Prerequisite: EE 3302 or equivalent.

Offering Frequency: Based on student interest and instructor availability

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