# 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
Cross-listed with:
BIOL-4220 Machine Learning For Environmental Biology
Prereqs:
none

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Simple View
Detailed View

| Spring | Summer | Fall
--- | --- | --- | ---
| (Session 1) | (Session 2) |
2026 |  |  |

2025

Machine Learning For Environmental Biology (4c)

- Jeremy Lynch Farrell

Seats Taken: 1/10

2024

Machine Learning For Environmental Biology (4c)

- Jeremy Lynch Farrell

Seats Taken: 5/10

2023

Machine Learning For Environmental Biology (4c)

- Jeremy Lynch Farrell

Seats Taken: 2/10

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

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