Lectures related to Experimental Designs: Aliases and Model Decomposition

Singular Value Decomposition

Covers the singular value decomposition theorem, explaining the construction of orthonormal bases and the matrix representation of the decomposition.

Singular Value Decomposition

Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.

Cholesky Factorization: Theory and Algorithm

Explores the Cholesky factorization method for symmetric positive definite matrices.

Chaos and Lyapunov Exponents: Analyzing Predictability

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

Construction of an Iterative Method

Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.

Orthogonal Families and Projections

Explains orthogonal families, bases, and projections in vector spaces.

Untitled

Spectral Decomposition and SVD

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.