Lecture
This lecture covers the key concepts of Principal Component Analysis (PCA), including its properties of reducing data dimensionality and extracting features. The instructor explains how PCA uses existing correlations across data points and its applications as a compression method for data storage, preprocessing before classification, and exercise scenarios. The exercises involve reducing the dimensionality of datasets, preprocessing for classification, interpreting PCA projections and eigenvectors, and choosing optimal eigenvectors. Additionally, PCA is applied to images and faces to demonstrate its effectiveness in feature extraction and data analysis.