Lectures related to Linear Transformations: Matrices and Bases

Covers vector spaces, subspaces, kernel, image, linear independence, and bases in linear algebra.

Introduces linear applications, span, kernels, and images in vector spaces with illustrative examples and theorems.

Covers the basics of linear mapping and coordinate systems.

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

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

Explores bases, dimensions, and matrix ranks in vector spaces with practical examples and proofs.

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