Lecture

Singular Value Decomposition (SVD)

In course

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Description

This lecture covers the Singular Value Decomposition (SVD) in detail, including properties of matrices, dimensions, bases, and system linearity. It also discusses the concepts of orthogonality, eigenvalues, and the rank theorem. The lecture concludes with applications of SVD in solving linear systems and matrix inversion.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_pqrkrzxh

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Ontological neighbourhood

Mathematics

Algebra: Linear algebra

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