Matrix Calculus: Rank and Decomposition

Related lectures (27)

Page 2 of 3

Covers matrices, linear applications, vector spaces, and bijective functions.

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

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

Explores the properties of invertible matrices, including unique solutions and linear independence.

Covers the definitions and properties of matrices, including matrix operations and determinants.

Covers linear transformations, matrices, kernels, and properties of invertible matrices.

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

Explores determinants in 3D space and their role in linear algebra.

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

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