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Related lectures (24)

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Covers matrix multiplication, properties, and inverses in linear algebra.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Explores diagonalization of symmetric matrices and their eigenvalues, emphasizing orthogonal properties.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.