Lectures related to Linear Algebra: Eigenvalues and Eigenvectors

Explores the reduction of a linear transformation with a single real eigenvalue.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores examples of diagonalization in linear algebra, focusing on eigenvalues and eigenvectors.

Introduces eigenvalues, eigenvectors, and similar matrices, emphasizing diagonalization and geometric interpretations.