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Related lectures (25)

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Covers the factorization of a matrix and the least squares method.

Covers orthogonal linear maps, orthogonal matrices, invertibility, and least squares solutions in Euclidean spaces.

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

Covers the Factorisation QR theorem and the Gram-Schmidt method for orthonormal bases.

Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.

Explains row-echelon and reduced row-echelon matrices and their role in simplifying system resolution.

Covers the concept of the matrix of cofactors and a formula to calculate the inverse of a matrix.

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

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Covers linear applications, matrices, transformations, and the principle of superposition.