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Linear algebra for advanced studies
·
Module 6
Singular Value Decomposition (SVD)
1
Singular Values and Vectors
Define singular values and left/right singular vectors of a matrix.
2
Singular Value Decomposition (SVD) of a Matrix
Construct the full and reduced Singular Value Decomposition (SVD) of a matrix.
3
SVD: Decoding Matrix Transformations
Interpret the geometric action of a matrix via SVD as a sequence of rotation, scaling, and rotation.
4
SVD for Rank, Kernel, and Image
Use SVD to determine the rank, kernel, and image of a matrix.
5
Pseudoinverse Computation with SVD
Compute the pseudoinverse of a non-square or singular matrix using SVD.
6
Low-Rank Matrix Approximation with SVD
Apply SVD to find the best low-rank approximation of a matrix.
Previous module
Spectral Theory for Symmetric & Hermitian Operators
Next module
Applications in Data Analysis