Clustering & Density Estimation

This lecture covers the concepts of dimensionality reduction, focusing on Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA). It also delves into nonlinear dimensionality reduction methods like Kernel PCA and t-SNE. The lecture then transitions into the topic of clustering, explaining K-means clustering and its properties. It further explores Gaussian Mixture Models (GMM) and non-parametric density estimation techniques like histograms and Kernel Density Estimation (KDE). The Mean Shift algorithm for clustering is also discussed, emphasizing its iterative approach to finding density maxima. Real-world examples and visualizations are used to illustrate these concepts.