Lecture
This lecture covers the concepts of unsupervised learning, focusing on clustering techniques such as hierarchical clustering, partitioning clustering, and semi-supervised learning. It explains the principles behind clustering, the different algorithms like k-means, and the challenges faced in clustering methods. The instructor discusses the applications of unsupervised learning in various fields, including data analysis, bioinformatics, and e-commerce. The lecture also delves into the complexities of clustering algorithms, the importance of similarity measures, and the process of estimating parameters in probabilistic clustering models.