Linear Maps: Basis and Applications

Related lectures (25)

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

Covers the basics of linear mapping and coordinate systems.

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

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

Explores forming bases, dependencies, and relationships in linear applications using invertible matrices and canonical bases.

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

Explains linear independence, basis, and matrix rank with examples and exercises.

Covers linear applications, matrices, transformations, and the principle of superposition.

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

Covers the matrix representations of linear applications and how to find representative matrices.