Explore the power of active sampling in unsupervised learning through this 52-minute lecture by Aarti Singh from Carnegie Mellon University, presented at the Simons Institute. Delve into topics such as big data systems, partially observed data, statistical learning tradeoffs, and Gaussian graphical models. Examine passive and active graphical model selection, incoherence and leverage scores, and random sampling for matrix completion. Investigate the computational complexity and effects of row coherence through simulations and theory. Learn about column subset selection, active matrix approximation, and active sampling for clustering. Gain insights into the latest research and applications in interactive learning and unsupervised machine learning techniques.