Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.
Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
