Orthogonal Matrices and Least Squares Method

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Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.

Covers the organization of linear algebra course and exercises for civil engineering and environmental sciences students.

Covers the determination of matrices associated with linear transformations and explores the kernel and image concepts.

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

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

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

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

Emphasizes the importance of using orthogonal bases in linear algebra for representing linear transformations.

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