Unsupervised Learning: Dimensionality Reduction

This lecture covers the concepts of unsupervised learning, focusing on dimensionality reduction techniques such as Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA). It explains how these methods aim to reduce the number of features while preserving the most important information in the data. Additionally, it introduces Kernel PCA as a nonlinear dimensionality reduction approach and discusses the trade-offs between linear and nonlinear methods. The lecture also touches on the challenges of working with high-dimensional data and the need for effective data representation techniques.