Principal Component Analysis: Dimensionality Reduction

This lecture covers Principal Component Analysis (PCA) as a technique for dimensionality reduction, focusing on unsupervised feature selection and data preprocessing. The instructor explains the PCA algorithm, the process of reducing the dimension of feature vectors, and the interpretation of results through encoding and decoding. The lecture also delves into the geometric and algebraic derivations of PCA, illustrating the concept through matrix representations and the reconstruction of original data from reduced vectors.