BST 6015 Machine Learning and Computational Statistics 3.0 Credits
This course develops understanding of commonly used machine learning algorithms and computational statistical methods. It covers numerical techniques, Expectation Maximization (EM) and Markov Chain Monte Carlo (MCMC) algorithms, simulation and resampling-based inference, penalized regression and classification, support vector machines, neural networks, kernel methods, model selection, clustering, boosting, CART and random forests, and ensemble learning. Connections to foundational concepts in probability and statistical inference are emphasized throughout.
Repeat Status: Not repeatable for credit
Prerequisites: (BST 5010 [Min Grade: C] or BST 551 [Min Grade: C]) and (BST 5012 [Min Grade: C] or BST 569 [Min Grade: C])