Orthonormality

Related lectures (31)

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Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Covers Dirac's notation in linear algebra and the tensor product concept in Hilbert spaces.

Covers the calculation of Fourier coefficients for periodic complex signals and the concept of a complete orthonormal system.

Explores parametric equations in 2D coordinates and their geometric interpretations in different reference frames.

Delves into the proof of the convergence rate theorem for an ergodic Markov chain, emphasizing eigenvalues and detailed balance properties.

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

Explores scalar product in an orthonormal basis and its applications in 3D space.

Explores cross-correlation, convolution, signal approximation, orthonormal functions, and orthogonal approximation theorem in signals and systems.

Explores cross-correlation, signal detection, Hilbert spaces, orthogonal approximation, and image compression using DCT.

Explores the diagonalizability of symmetric matrices and their eigenvectors in an orthonormal basis.