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
Meeting Patterns
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
Restrictions:
Restriction: Graduate standing; Senior Undergraduates with permission from instructor
