Lecture
This lecture covers unsupervised behavior clustering and dimensionality reduction techniques, focusing on algorithms like K-Means, DBSCAN, Gaussian Mixture Model, and Hierarchical Clustering. It explores the challenges of high-dimensional data, the curse of dimensionality, and the need for dimensionality reduction. The presentation also delves into deep-learning-based clustering methods and provides insights on implementing clustering algorithms using libraries like scikit-learn.