Probabilistic Machine Learning

Catalog description

An introductory course to topics in machine learning studied from a probability theory viewpoint. Covers an overview of basic probability, inference and estimation, regression algorithms, Markov chains, inference for Markov models and the EM algorithm, Markov decision process, and reinforcement learning. In addition to covering mathematical and algorithmic details, the course includes several hands-on projects to implement the machine learning algorithms.