Unsupervised Machine Learning: Clustering Basics

This lecture covers the basics of unsupervised machine learning, focusing on clustering techniques such as K-means, Gaussian Mixture Models, and DBSCAN. The instructor explains how these algorithms work, their applications, and the key differences between them. Starting with an introduction to unsupervised learning, the lecture delves into the details of K-means, including the algorithm, implementation, and convergence criteria. It then moves on to Gaussian Mixture Models, discussing the Gaussian model, likelihood function, and parameter optimization. Finally, DBSCAN is introduced as a clustering method that does not require a predefined number of clusters, with a detailed explanation of the algorithm and hyperparameter sensitivity.