Lectures related to Vector Spaces in R2 and R3

Covers the concept of dimension in a vector space and bases.

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

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

Covers matrix kernels, images, linear applications, independence, and bases in vector spaces.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.