Gaussian Mixture Models & Noisy Signals

This lecture covers the concept of Gaussian mixture models and noisy signals. It explains how to model samples generated by different classes using Gaussian distributions. The instructor provides MATLAB code for generating i.i.d. samples and discusses parameter estimation of the Gaussian mixture model. Additionally, the lecture delves into denoising noisy signals using a probabilistic approach, estimating the original signal, and maximizing likelihood and posteriori functions.