EBIO5460 Machine Learning for Ecology Spring 2025
Department of Ecology and Evolutionary Biology
University of Colorado, Boulder
Instructor: Dr Brett Melbourne, [email protected]
Pronouns: he, him, his
- Syllabus
- Timetable: what topics we covered and when
- Location: Muenzinger Psyc & Biopsych E114, Tue/Thu 3:30 - 4:45
- Office hours: any time via zoom, arrange by email
- Zoom: 995 5569 4569, as needed and for office hours
- Text: James et al. 2021 (2023 corrected version; both R and Python editions)
- Google Drive: anything not open access, audio and zoom recording links, collaborative notes etc
- Class recordings: password required, by request
- Piazza: help, questions, discussion
- Zotero library: collection of papers
This repository includes lecture slides (pdf), code, and homework instructions. For the most part, where code is concerned you want to view the markdown (.md) files in your web browser from GitHub.com. These markdown files are knitted from the R code. You can also run the R or Python code on your computer from the .R or .py files.
This is the second semester in a graduate-level "data science for ecology" sequence. Semester 1 is here.
Previous iteration: Machine Learning for Ecology 2024.
Awesome papers that started as machine learning projects in previous iterations of this class:
Martin O, Nguyen C, Sarfati R, Chowdhury M, Iuzzolino ML, Nguyen DMT, Layer RM, Peleg O (2024). Embracing firefly flash pattern variability with data-driven species classification. Scientific Reports 14: 3432. https://doi.org/10.1038/s41598-024-53671-3.
Ramoneda J, Stallard-Olivera E, Hoffert M, Winfrey CC, Stadler M, Niño-García JP, Fierer N (2023). Building a genome-based understanding of bacterial pH preferences. Science Advances 9: eadf8998. https://doi.org/10.1126/sciadv.adf8998.