Principles of Machine Learning

Catalog description

This course covers maximum likelihood estimation, linear discriminant analysis, logistic regression, support vector machine, decision tree, linear regression, Bayesian inference, unsupervised learning, and semi-supervised learning. Students are not allowed to take both MATH 405 and STATS 302 because of the content overlap. Students who are planning to major in Data Science should take STATS 302. 001263