Clustering: K-means & LDA

This lecture covers the concepts of clustering using K-means and Fisher Linear Discriminant Analysis (LDA). It explains the intuition behind PCA, the objective of PCA, and how to maximize variance. It also delves into Kernel PCA and provides examples of PCA applications. The lecture introduces the K-means clustering algorithm, its properties, and the Elbow method for determining the optimal number of clusters. Additionally, it discusses Fisher LDA, its objective functions, and how it separates different classes. The lecture concludes with an overview of spectral clustering and the normalized cut method.