Lectures related to QR Factorization and Least Squares

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

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

Explores Gram-Schmidt process, orthogonal projection, QR factorization, and least squares solutions for linear systems.

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

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

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

Explains the QR factorization theorem and demonstrates the Gram-Schmidt procedure with an example.

Explores matrix equivalence theorems for systems of equations and least squares solutions.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers the QR factorization of a matrix A into Q and R.

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.