Statistics

This course explores the foundations of Supervised Machine Learning through a rigorous probabilistic and statistical lens. Students will learn how to predict labels and continuous targets from observed data, treating each algorithm not as a black box but as an estimator with explicit distributional assumptions, an associated likelihood, and quantifiable error behavior.

This course explores the foundations of Unsupervised Machine Learning and Representation Learning through a rigorous probabilistic and statistical lens. Students will learn how to extract patterns, model underlying distributions, and discover hidden structures in unlabeled data using advanced statistical frameworks and information theory.