Lectures related to Singular Value Decomposition: Applications and Interpretation

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Covers the theorems related to orthogonal projection and orthonormal bases.

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

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

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

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

Explains linear independence, basis, and matrix rank with examples and exercises.

Explains orthogonal families, bases, and projections in vector spaces.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

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

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

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

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.