In this math module, we will focus on how linear algebra, statistics, and signals and 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.