Lectures related to Linear Mapping Basics

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

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

Covers the determination of matrices associated with linear transformations and explores the kernel and image concepts.

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

Covers linear transformations using matrices, focusing on linearity, image, and kernel.