Orthogonal Projections: Theory and Applications

Related lectures (29)

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Explains orthogonal projection on a vector subspace in Euclidean space.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Covers the concept of eigenvalues and eigenvectors in linear algebra, focusing on the properties of endomorphisms.

Covers the analytical expression of orthogonal projections and reflections in 2D space.

Explores spectral and singular value decompositions of matrices.

Explains orthogonal matrices, Gram-Schmidt process, and best vector approximation in subspaces.

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

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Covers the theorems related to orthogonal projection and orthonormal bases.

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