|Location:||Online / Asynchronous|
|Meeting Time:||K hr: TTh 2:30-3:50|
|Offered this year?||Yes|
|When Offered?||Every year|
How can artificial systems learn from examples, and discover information buried in massive datasets? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian and maximum likelihood parameter estimation, regularization and sparsity-promoting priors, kernel methods, the expectation maximization algorithm, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples from vision, language, bioinformatics, and information retrieval.
Prerequisites: Comfort with basic multivariable calculus, an introductory programming course CSCI 160, 180, or 190. MATH 100, 170, 180, 190, 200, or 350. CSCI 1450, APMA 1650, or APMA 1655. CSCI 530, MATH 520, or MATH 540; or permission of instructor.