Linear Algebra: Bases and Transformations

Related lectures (27)

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

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

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

Explains orthogonal families, bases, and projections in vector spaces.

Explains the step-by-step process of finding the singular value decomposition of a matrix.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Explores algebraic and geometric multiplicities of eigenvalues in linear algebra.

Covers determining vector spaces, calculating kernels and images, defining bases, and discussing subspaces and vector spaces.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Explores matrices, change of bases, eigenvectors, eigenvalues, and vector spaces.