Machine Learning Foundations

1.5 Credits · Level: Graduate

Machine learning refers to a set of tools for modeling and understanding complex datasets that builds on statistics and computer science. This course introduces the main tools developed in this field. The course first covers traditional regression methods where the dependent variable is either a continuous or a discrete variable. It then introduces students to resampling methods, such as cross-validation and bootstrap. Finally, it covers model selection and regularization methods. These include subset selection methods, shrinkage methods, and principal component regressions. The course will emphasize a rigorous treatment of the material as well as their practical implementation for business.