Lectures related to QR Factorization and Least Squares

Covers the Factorisation QR theorem and the Gram-Schmidt method for orthonormal bases.

Explains the step-by-step process of finding the singular value decomposition of a matrix.

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

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Covers the LU decomposition algorithm, transforming a matrix into L and U.

Covers the LU decomposition method applied to linear systems, presenting the system in two steps.

Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.