Explores the U(1)-extension of Anosov diffeomorphisms and the proof of exponential mixing through uniform contractivity and cancellation by complex phases.
Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
