Lectures related to Linear Algebra: Decomposition Theorems

Explores the application of matrices and eigendecompositions in networks.

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

Delves into singular value decomposition and its applications in linear algebra.

Covers the concept of diagonalization of matrices through the study of eigenvectors and eigenvalues.

Explores eigenvalues and eigenvectors in matrices through examples and calculations.

Covers eigenvalues and eigenvectors, explaining their importance in linear algebra.

Covers bases, transformations, and matrix decompositions in linear algebra.

Explores the application of linear algebra in quantum mechanics, emphasizing vector spaces, Hilbert spaces, and the spectral theorem.

Covers phase portraits, eigenvalue decomposition, Jordan decomposition, and stable nodes in non-linear systems.