In this math module, we will focus on how linear algebra, statistics, and signals & systems techniques can be used to interrogate data from biological and engineering experiments. The course also serves as a primer for basic machine learning concepts that would be useful for higher-level ML courses such as EC 503, EC 523 CS 542, BE 562, or BE 559. Topics include: Gradient descent, Levenberg-Marquardt algorithm (nonlinear least-squares), logistic regression & classifications; the ANOVA table, multi- factor regression, and intro to the general linear model (GLM). Prior exposure to linear algebra (BE 601 equivalent), basic probability and statistics (BE 200 equivalent), and working knowledge of a programming language (Matlab, Python, etc.) is highly recommended.

Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.