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Related lectures (26)

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Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

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

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.