Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018
Andrew Ng explains Principal Component Analysis (PCA) as a classic non-probabilistic algorithm for linear dimensionality reduction that identifies principal axes of variation through eigenvector decomposition of the covariance matrix, while cautioning that practitioners often apply it unnecessarily.