Lectures related to Factorisation QR: Gram-Schmidt Process

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

Covers the Singular Value Decomposition theorem and its applications in practice.

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

Covers the theorems related to orthogonal projection and orthonormal bases.

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

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

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

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

Covers the fundamentals of Singular Value Decomposition, including properties, applications, and error measurement.