Principal Component Analysis: Covariance Matrix and Eigenvalues

This lecture covers the concept of Principal Component Analysis (PCA) applied to data with a focus on maximizing variance. Topics include the formulation of the augmented Lagrangian, empirical covariance matrix calculation, and finding the second PC component through eigenvalues. The lecture also discusses the eigen vector corresponding to the largest eigenvalue and its significance.