|Meeting Time:||K hr: TTh 2:30-3:50|
|Exam Group:||11: 14-MAY-2019 Exam Time: 02:00:00 PM|
|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 CSCI0040, CSCI0150, CSCI0180, or CSCI0190), an introductory probability course (CSCI0450, APMA1650, or MATH1610), and an introductory linear algebra course (CSCI0530, MATH0520, or MATH0540); or permission of instructor.