Lectures related to Linear Algebra: Normal Equations and Symmetric Matrices

Diagonalization of Matrices: Theory and Examples

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Diagonalization of Matrices

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

Eigenvalues and Diagonalization

Covers eigenvalues, eigenvectors, and diagonalization of matrices.

Symmetric Matrices: Diagonalization and Orthogonality

Explores symmetric matrices, diagonalization, and orthogonality properties, emphasizing simplicity and geometric relationships.

Spectral Theorem Recap

Revisits the spectral theorem for symmetric matrices, emphasizing orthogonally diagonalizable properties and its equivalence with symmetric bilinear forms.

Orthogonality and Least Squares Methods

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Diagonalization of Symmetric Matrices

Explores the diagonalization of symmetric matrices and the orthogonality of eigenvectors.

Symmetric Matrices: Eigenvalues and Diagonalization

Covers symmetric matrices, eigenvalues, and diagonalization process for spectral theorem applications.