Lecture
This lecture covers the application of Gaussian Mixture Models in data classification, focusing on denoising noisy signals and estimating the original signal using maximum likelihood and maximum a posteriori approaches. It explains the likelihood function of Gaussian mixture models, the maximization of likelihood functions, and the EM algorithm for maximizing likelihood functions. The lecture also delves into the EM algorithm for i.i.d. classes, the maximization of likelihood functions for i.i.d. classes, and the EM algorithm for i.i.d. classes. Additionally, it discusses the EM algorithm for maximizing the likelihood function of Gaussian mixture models, the EM algorithm for i.i.d. Gaussian mixture models, and the EM algorithm for Markovian Gaussian mixture models.