ENBC 321
Machine Learning for Data Analysis
Instructs students in the fundamentals of machine learning methods through examples in the biological phenomenon and clinical data analysis. This course is designed to share knowledge of real-world data science and aid to learn complex machine learning theory, algorithms, and coding libraries in a simple way. Students will learn the machine learning theory, but they will also get hands-on practice building their models using programming tools such as Python and R.
Prerequisites & course information
Prerequisite: Minimum grade of C- in ENBC311 and ENBC332 . Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Sources
- Course catalogue ↗2026-2027