Unsupervised Learning: PCA & K-means

This lecture covers unsupervised learning techniques such as Principal Component Analysis (PCA) for dimensionality reduction and K-means for clustering data. It explains how PCA creates new features as a linear combination of original features and how K-means groups data points into clusters. The lecture also touches on other dimensionality reduction techniques like Linear Discriminant Analysis (LDA) and Generalized Discriminant Analysis (GDA). Practical tips for PCA implementation and the formulation of optimization problems are discussed, along with the importance of covariance matrices in data analysis.