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Modern ecology increasingly joins traditional fieldwork with large datasets, computer models, and artificial intelligence. Ecologists now draw on information from environmental sensors, camera traps, acoustic monitors, animal-tracking systems, remote sensing, and global biodiversity databases. Undergraduate students therefore need more than familiarity with ecological theory: they need a practical understanding of how machine-learning models find patterns, make predictions, and sometimes fail. This Foundations Course provides an introductory, hands-on guide to machine learning and AI for the next generation of ecologists, embedding computational methods within authentic questions about organisms, populations, communities, and changing environments.
Through custom-built interactive applications, ecological case studies, and progressively more independent investigations, students explore the logic behind methods ranging from regression, decision trees, clustering, and similarity analysis to neural networks, computer vision, recurrent models, and reinforcement learning. They move beyond simply running algorithms to preparing ecological data, building and evaluating models, interpreting results, and asking whether the conclusions make biological sense. Along the way, students develop portfolios of Python code and ethical reflections that demonstrate not only technical competence, but also the scientific judgment needed to use AI responsibly. The goal is not to replace the field notebook or the ecologist, but to add powerful new tools to the ecological toolkit and prepare students to investigate living systems in an increasingly data-rich world.
Installation Software
Installation Software
ChemLeanEcologySetup
ChemLeanEcologySetup
Mac Installer (700 MB). Video demo by Jack Sticha
Linux Installer (AppImage) (Coming soon)
TextBook
TextBook
P.S. Mork, M.R. Bush, J.R. Mach, D.J. Ulness Foundations of Machine Learning for Ecology.
Free PDF file PDF is a near final draft. Check back shortly for final draft.
Softcover ($43) from Lulu, Amazon, Barnes and Noble This to be available shortly
GoogleLM's take on the book.
(LM Video)
About the Authors
About the Authors
Associate Professor of organic chemistry of 33 years at Concordia College in Moorhead, Minnesota. She is a passionate advocate for her students. Students report they leave her classes feeling like a scientist. She authored an honors-level high school chemistry textbook. She loves incorporating new technology in her classes and is committed to equiping chemistry majors for the AI-driven future.
Mike began his role as assistant professor at Concordia College in 2022. Prior to that, he worked in the non-profit world, mostly with the Audubon Society, and some agency work doing land management. His PhD focused on the responses of fishes and aquatic macroinvertebrates to large-scale restoration projects in the Everglades of Florida. Mike teaches Ecology and some other field courses, including Limnology and Conservation Biology. His current research examines the role of landscape configuration on small stream fishes.
Associate Professor of Biochemistry and Chair of the Chemistry Department at Concordia College in Moorhead, Minnesota, where she has taught since 2001. An enthusiastic early adopter of educational technology, she has a strong sense for how to guide students in using these tools to enhance their learning. She is passionate about fostering independent learning and mentoring students through course-based undergraduate research experiences in biochemistry. She has an interest in molecular modeling and protein structure prediction, using computational tools to explore and advance biochemical research.
Professor of physical chemistry at Concordia College in Moorhead, Minnesota, where he has taught since 1998. His research interests include nonlinear optical spectroscopy, mathematical physics, and the dynamics of liquids. He is passionate about helping students develop both a curiosity about nature and the confidence to apply their knowledge in the evolving field of chemistry.
Ulness is co-leader of The Concordia Bat Project.
You might also be interested in our free chemistry textbook: Machine Learning for Chemistry or our humanities textbook: Machine Learning for the Humanities.
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