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.

Spring 21

Course offered in department space

FALL 2020

Instructors

Classes Start:
August 10, 2020
Classes End:
November 17, 2020
Location:
Room To Be Announced
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