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Related lectures (30)
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Linear Regression: Least Squares Method
Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.
Symmetric Matrices: Properties and Decomposition
Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.
Orthogonal Projection: Spectral Decomposition
Covers orthogonal projection, spectral decomposition, Gram-Schmidt process, and matrix factorization.
Diagonalization Techniques: Jacobi Method
Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.
QR Factorization
Explains the QR factorization theorem and demonstrates the Gram-Schmidt procedure with an example.
Matrix Decomposition: Triangular and Spectral
Covers the decomposition of matrices into triangular blocks and spectral decomposition.
Untitled
Linear Algebra: Orthogonal Projection and QR Factorization
Explores Gram-Schmidt process, orthogonal projection, QR factorization, and least squares solutions for linear systems.
Spectral Decomposition of Symmetric Matrices
Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.
Circular Motion: Dynamics and Speed
Explores circular motion dynamics, speed calculations, and vector decomposition in advanced physics.