MATH-356 Statistical Methods in Machine Learning
This course covers statistical methods in machine learning such as decision trees, random forests and support vector machines The course will use a project-based approach to give students hands-on experience using these techniques by analyzing large and complex real-world datasets. More importantly, they will learn the statistical principles behind these procedures, such as loss functions, maximum likelihood estimation and bias-variance trade-off as well as why these principles matter in real world settings.
Standard Course (40)
Prerequisite
Math 254 and Math 351
Offered
Spring