AEC 510 Machine Learning Approaches in Biological Sciences Section: 001
Course Description
A wide range of high-throughput technologies are now being used to generate data to answer an ever-increasingly diverse set of questions about biological systems. The next great challenge is integrating data analysis in a systems biology approach that utilizes novel supervised machine learning methods, which accommodate heterogeneity of data, are robust to biological variation, and provide mechanistic insight. The course will not focus on detailed mathematical models, but instead on how these machine learning tools may be used to analyze biological data, in particular gene and protein expression.
FALL 2021
Instructors
- Classes Start:August 16, 2021
- Classes End:November 29, 2021
- Location: 00283 David Clark Labs
- Class Days: W
- Class Start Time: 9:35am
- Class End Time: 11:25am
- Class Type: Lecture
- Credits: 2.00
- Delivery Method: In Person
- Restrictions: Restriction: Graduate standing; Senior Undergraduates with permission from instructor