Lectures related to Context and applications: Simple applications

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

Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Explores the definition of connections for smooth vector fields on manifolds.