Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.