Lecture
Singular Value Decomposition: Theory and Applications
In course
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Description
This lecture covers the theory and applications of Singular Value Decomposition (SVD) for matrices, including the full-form and reduced-form SVD, matrix properties derived from SVD, solving linear systems using SVD, least squares solutions, matrix pseudoinverse, and polynomial fitting. It also explores statistical interpretations, data with errors, weighted least squares regression, goodness of fit, and examples of data fitting with overfitting and underfitting.
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