Subspace-based approximations

Related lectures (27)

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

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

Covers tensor products, symmetric power, and exterior power of vector spaces, including properties and applications.

Explores signal approximation, orthogonal bases, Fourier series, correlation, and vector geometry.

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

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Explores vector approximation, signal detection, correlation, Fourier series, and signal comparison in signals and systems.

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

Explains orthogonal projection on a vector subspace in Euclidean space.

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