Machine Learning for Environmental Biology

BIOL-6220

This course is designed to create an applied learning environment to introduce students to large scale datasets in the environmental field and learn advanced techniques for analyzing them. Students will learn multivariate data exploration techniques, evaluate the quality of large datasets, and analyze the data using machine learning techniques. Specifically students will propose, develop, and finalize projects where they will apply machine learning approaches to datasets to understand complex environmental biology processes. Along with these topics, students will learn to critically read current scientific literature relevant to their projects.

4 credits
Prereqs:
none

Past Term Data

Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Spring Summer Fall
(Session 1) (Session 2)
2023
Machine Learning For Environmental Biology (4c)
  • Jeremy Lynch Farrell
Seats Taken: 0/10
2022
Machine Learning For Environmental Biology (4c)
  • Jeremy Lynch Farrell
Seats Taken: 3/10
2021
Machine Learning For Environmental Biology (4c)
  • Jeremy Lynch Farrell
Seats Taken: 4/10
2020
Machine Learning For Environmental Biology (4c)
  • Jeremy Lynch Farrell
Seats Taken: 3/10
2019
Machine Learning For Environmental Biology (4c)
  • Jeremy Lynch Farrell
Seats Taken: 3/10
2018
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