Principal Component Analysis: Dimension Reduction

This lecture covers Principal Component Analysis (PCA) as a classic method for dimension reduction in datasets. It explains the central idea of PCA, the process of variable extraction, the calculation of principal components, and the selection of the number of components to retain. The lecture also discusses the representation of data in reduced dimensions, the importance of reducing dimensionality for supervised learning algorithms, and methods to avoid overfitting during model selection and evaluation.