Bayesian Data Science – Computation & Applications

3 credits · College of Engineering catalogue

Advisory Prerequisite: Knowledge of basic statistics at the level of IOE 265 or STATS 250. Knowledge in linear algebra at the level of MATH 417. Proficiency in data visualization using R, Python, or MATLAB. Enforced Prerequisite: None. Credit Exclusions: Student may only receive credit for one course , IOE 567 or IOE 667. (3 credits) Offers a comprehensive learning of Bayesian Fundamentals, Single and Multivariate Parameter Models, Bayesian Model Evaluation, Bayesian Decision Analysis, Modern Markov Chain Monte Carlo, Bayesian Regression, Variational Inference (including Posterior Inference via Optimization, Gaussian Processes, Bayesian Deep Learning, Bayesian Optimization, and Distributed Bayesian Analysis.) Significant focus is also placed on applications of Bayesian data science and modeling of real-life data using coding software.