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Tutorial 6: Exploring other applications#

Week 2, Day 5, AI and Climate Change

Content creators: Deepak Mewada, Grace Lindsay

Content reviewers: Mujeeb Abdulfatai, Nkongho Ayuketang Arreyndip, Jeffrey N. A. Aryee, Paul Heubel, Jenna Pearson, Abel Shibu

Content editors: Deepak Mewada, Grace Lindsay

Production editors: Konstantine Tsafatinos

Our 2024 Sponsors: CMIP, NFDI4Earth

Tutorial Objectives#

Estimated timing of tutorial: 40 minutes

In this tutorial, you will

  • Discuss the many ways AI/machine learning can be applied to problems related to climate change

  • Learn about resources in this domain

  • Discuss issues when deploying an AI system on real problems


# imports
import matplotlib.pyplot as plt

Figure Settings#

Hide code cell source
# @title Figure Settings
import ipywidgets as widgets  # interactive display

%config InlineBackend.figure_format = 'retina'

Video 1: Exploring other applications#

Section 1: Exploring other applications#

As discussed in the video, the objective of this tutorial is to help you to explore and think critically about different climate-related applications, frame problems in data science terms, and consider the potential impact of machine learning solutions in the real world. By the end of this tutorial, participants should have a better understanding of how to identify relevant problems and applications and consider the ethical and practical implications of using machine learning in a given domain.

Section 1.1: Finding Other Applications#

Now that you know the basics of how machine learning tools can be applied to climate-related data, in this tutorial, you will explore more climate-related problems and think about how you would approach them using machine learning tools. Specifically, go to the Climate Change AI summaries page ( and scroll to the Societal Impacts section. As a group, pick a topic you would like to discuss further and read the section on it.

Section 1.2: Questions to Consider#

Think about the example applications you just read about and reflect on these questions as a group.

  • What kind of data would a machine learning algorithm need to train on for this application? What kind of domain experts would you want to interact with when building a model for this application?

  • What type of generalization would you want to test for? How would you do so?

  • Who would be most impacted by the use of this model in the real world? Who would be held accountable for the impacts from the model’s use?


In this tutorial, we explored the importance of exploring more applications, framing problems in data science terms, and considering impact. We encourage you to continue exploring applications and framing problems in data science terms. Remember to consider the ethical implications of using applications and ensure that the models are appropriately and fairly integrated with stakeholders.


  • Climate change AI wiki.

  • If you want to gain more skills in building machine learning models, check out the Neuromatch Deep Learning Course