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Related lectures (27)
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Orthogonal Projection: Spectral Decomposition
Covers orthogonal projection, spectral decomposition, Gram-Schmidt process, and matrix factorization.
Orthogonal Groups
Covers the concept of orthogonal groups in finite-dimensional vector spaces and their special properties.
Symmetric Matrices: Properties and Decomposition
Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.
Diagonalization of Symmetric Matrices
Explores diagonalization of symmetric matrices and their eigenvalues, emphasizing orthogonal properties.
Orthogonal Families & Projections
Covers orthogonal families and projections in vector spaces, including the Gram-Schmidt process.
Aggregation queries and sorting
Covers aggregation queries and sorting of results in Geographic Information Systems.
Linear Applications and Eigenvectors
Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.
Orthogonality and Least Squares Methods
Explores orthogonality, norms, and distances in vector spaces for solving linear systems.
Orthogonality and Inequalities
Explores symmetrical bilinear forms, the Pythagorean theorem, inequalities, and orthogonal matrices.
Projection in Vector Spaces
Explores the generalization of projection in vector spaces and its unique properties, emphasizing its role in finding the closest vector in a subspace.