Linear Algebra: Reduction of Linear Application

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

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

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

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

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

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

Covers the basics of linear mapping and coordinate systems.

Covers the concept of basis, linear transformations, matrices, inverses, determinants, and bijective transformations.

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

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.