CPSC 540 - Statistical Machine Learning I

This course covers a range of topics in Statistical Machine Learning including the Mathematical foundations of machine learning, data preprocessing (including scaling, dimensionality reduction, and imputation), Bayesian and Frequentist inference (including particular-use models which may include Generalized Linear Models, Generalized Additive Models, Mixed Effect Models, Survival Analysis, and Item Response Theory), longitudinal data models (including repeated measures and time series), and ethical considerations that arise through the application of these topics. This course assumes students have completed undergraduate-level coursework in probability, statistics, linear algebra, and computer programming. Letter grade. (Offered as needed.) 3 credits