Lectures related to Matrix Representations of Linear Applications

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

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

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Covers operations on linear applications in R³, including definitions, behavior of compositions, and determinants.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Covers the norm of a matrix, operator, singular values, and unitary matrices in linear algebra.