Statistical Learning and Data Analytics

Introduction to the goals and tools of machine learning and data analytics. Overview of machine learning on diverse data acquired by: sensor networks, physiological devices, etc. Fundamental learning models. Applications: decision support, computer vision, recommender systems. Performance analysis by using probabilistic approach. Bayesian and frequentist machine learning. Classification and regression. Linear regression, Ridge regression, Lasso. Parameter estimation and Bayesian regression. Generalized linear models. Neural Networks. Learning from unlabeled data: probabilistic clustering, blind signal separation and feature extraction. Graphical models. Techniques for handling missing and corrupted data. Deep learning, transfer learning, online learning.