This course will introduce Bayesian approaches to statistical modeling as well as the computational methods necessary to implement these approaches in research and applications. We will cover methods of statistical learning and inference for a variety of subject areas. Students will have the opportunity to apply these concepts and methods in the context of their own research or area of application in the form of a term project.
ENGS 93 or comparable course in probability and statistics; previous programming experience with Matlab, C, S, R, Julia, or similar language. (MATH/COSC 71, ENGS 91, COSC 70/170 are examples for appropriate ways to fulfill the programming requirement.) We will use the R language for code discussions and assignments. R is open source, widely used in statistics, and relatively easy to learn. The prerequisites can be replaced by a permission from the instructor.
This course was previously offered as ENGG 107 and will be offered as ENGS 107 after the 2023-2024 academic year.