CHE 2475 - STATISTICS AND COMPUTATIONAL METHODS FOR SYS BIO

Minimum Credits: 3
Maximum Credits: 3
Statistics and computational modeling are major drivers of the modern economy and critical tools for discovery in scientific research. These two fields are linked in a common goal: how do we make competent decisions in uncertain, complex, and connected environments? With the rapid expansion of machine learning technologies, it has become incredibly important for engineers and scientists to understand the basic methods underlying these algorithms and to be able to critically assess algorithm performance. This course will cover the basic theory and application of three related areas: statistics, dynamic simulation and machine learning classification. While theory will be discussed, the proper application of these tools and the methods for critically analyzing the results will be the primary focus of the course. The statistical and computational tools to be discussed are agnostic in their application, important to all research areas. However, in the course, the application of these tools will be to problems in biology and medicine. By the end of the course, students will be able to identify the proper statistical tests to be used for various scenarios, be able to develop customized tests for non-standard data types (bootstrapping), understand the computational challenges related to dynamic simulations of nonlinear systems, and be able to apply and evaluate off-the-shelf machine learning algorithms.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

Click here for class schedule information.

Add to Portfolio.

Close Window