Clustering: Unsupervised Learning

This lecture introduces the concept of clustering in machine learning, focusing on algorithms like hierarchical clustering, k-means, and density-based clustering. It covers the importance of clustering algorithms, evaluation methods, and practical applications such as market segmentation, social network analysis, and image segmentation. The lecture also explains the notation used in clustering, the significance of centroids and medoids, and metrics like homogeneity, separability, Davies-Bouldin index, and silhouette coefficient for evaluating clustering quality.