Lectures related to Matrix Representations of Linear Applications

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Covers the definitions and properties of matrices, including matrix operations and determinants.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

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

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers matrices, linear applications, vector spaces, and bijective functions.