Explores diagonalization of matrices through eigenvalues and eigenvectors, emphasizing distinct eigenvalues and their role in the diagonalization process.
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
