Lectures related to Orthogonal Diagonalization

Covers the concept of symmetric matrices, orthogonal bases, and eigenvectors.

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

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.

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