Welcome to the Earth Data Science and Machine Learning Course Series#

This is the online home for the second of the two summer 2025 courses in Columbia’s Climate School:

  • Computing and Research Methods for Climate Data Science (CLMT5405)

  • Machine Learning for Climate Science and Environmental Sustainability (CLMT5403)

Course Description#

The application of Machine Learning (ML) to climate science and environmental sustainability has become increasingly popular in recent years, promising to revolutionize how we analyze and address critical environmental challenges. This course will introduce students to the fundamental concepts and methods of ML, emphasizing their practical applications to climate science and environmental sustainability efforts.

Students will gain both theoretical knowledge and practical skills through hands-on experience with machine learning methods and coding. The course is designed to provide familiarity with the design, implementation, and evaluation of machine learning models towards addressing specific problems in climate science and sustainability. By working with real-world datasets, students will develop a deeper understanding of both the capabilities and limitations of ML tools in climate research and for evaluating environmental sustainability solutions. This course will cover essential topics such as data preprocessing, model selection, evaluation metrics, and the ethical implications of ML in climate science.

As ML tools become increasingly important to these application areas, this course will be invaluable for those looking to interact with scientists and engineers, manage scientific projects, and develop policies in the realm of climate science and sustainability.

Course objectives#

By the end of this course, students will:

  • Develop a strong foundation in ML concepts and techniques applicable to climate science and environmental sustainability challenges

  • Apply ML methods to analyze observational data sets, predict trends in environmental data, and identify patterns.

  • Critically assess the strengths and limitations of ML approaches to climate and environmental applications

  • Understand ethical considerations and policy implications related to ML applications to climate research and environmental science.

  • Explore future trends and emerging technologies

Course structure#

Most of our classes will consist of lectures and hands-on coding tutorials. You are expected to bring a laptop to class and follow along with the lectures, and be prepared to work together in groups on small coding assignments during class.

Policy on Use of ChatGPT and AI Tools#

In this course, we recognize that tools like ChatGPT and other AI assistants can support learning, especially in exploring new concepts, debugging code, and clarifying questions. However, it is essential that you develop your own skills and understanding.

Permitted Use:

  • You may use AI tools like ChatGPT for certain assignments where explicitly allowed—such as coding practice, project brainstorming, or exploratory data analysis.

  • When you do use AI tools, you must document how and where you used them (e.g., in a code comment or footnote). A simple statement like “Used ChatGPT to help debug the plotting code in section 2” is sufficient.

Ultimately, you are responsible for understanding and being able to explain all work you submit. If you cannot explain how a piece of code or analysis works, that is a signal to revisit the material, not rely more heavily on AI.