Empirical Methods: Machine Learning in Economics and Business

Undergraduate

Introduction to “modern” statistical learning and predictive modeling from statistical, theoretical, and computational perspectives; with applications. Topics may include linear and local (neighbour-based) regression methods, logistic regression and discriminant analysis methods of classification, cross validation and bootstrap resampling methods, model selection and regularization, classification trees, and support vector machines. Course Format: Lectures, lab, 4 hours per week. Restrictions: Open to ECON (single or combined), BBE majors and ECON minors until date specified in Registration guide. Prerequisite(s): ECON 2P90 or ECON 2P91. Course Notes: This course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term. Exclusions: Completion of this course will replace previous assigned grade and credit obtained in ECON 4P07.