Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.
Covers Latin Hypercube Sampling and Quasi Monte Carlo methods for stochastic simulation, explaining the goal of stratification and generating independent permutations.