Lectures related to Matrix Decompositions: LU, Cholesky, QR, Eigendecomposition

Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.

Covers Lyapunov exponents, chaos measurement, and perturbation analysis in dynamical systems.

Covers the QR factorization of a matrix A into Q and R.

Covers bases, transformations, and matrix decompositions in linear algebra.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Explores latent factor analysis for movie genre classification based on male versus female leads.

Covers the Factorisation QR theorem and the Gram-Schmidt method for orthonormal bases.

Covers the construction of an iterative method for linear systems by decomposing a matrix A into P, T, and P_A.