Singular Value Decomposition: Fundamentals

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Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Explores orthogonal projection in Euclidean space, emphasizing uniqueness and calculation methods.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explains orthogonal projection in linear algebra, focusing on transforming non-orthogonal bases into orthogonal ones.

Introduces orthogonal vectors, scalar product, Euclidean norm, Pythagorean theorem, and unit vectors.

Covers vector spaces, bases, and dimensions, exploring key concepts for solving systems of equations.

Explores changing bases in vector spaces and matrix representation of linear transformations.

Explains the method of least squares and normal equations for finding optimal solutions.

Covers the concepts of kernel and image of a linear transformation and their relationship with the rank of the matrix.

Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.

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