Lectures related to Gram-Schmidt Algorithm

Covers determining vector spaces, calculating kernels and images, defining bases, and discussing subspaces and vector spaces.

Covers orthogonal projection, spectral decomposition, Gram-Schmidt process, and matrix factorization.

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

Covers the basics of linear mapping and coordinate systems.