Explore the concept of smoothed analysis in unsupervised learning through a 21-minute IEEE conference talk. Delve into the smoothed analysis framework, its application to tensor decomposition, and its implications in high-dimensional settings. Learn about the challenges in bounding condition numbers and the intricacies of working with polynomials of perturbed vectors. Discover how this approach applies to robust subspace recovery, Hidden Markov Models, and robust tensor decomposition. Gain insights into the proof of Theorem 1, which underpins the theoretical foundations of this analytical method.