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A project-based learning course where teams of climate science and data science students collaborate to create machine learning predictive models for challenges inspired by LEAP's research

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LEAP Education

Climate Prediction Challenges

Spring 2022 (Syllabus)

A project-based learning course where teams of climate science and data science students collaborate to create machine learning predictive models for challenges inspired by LEAP's research


Project cycle 1: Jupyter Notebook for Exploratory Data Analysis

(starter codes)

Week 1 (Jan 18)

Week 2 (Jan 25)

Week 3 (Feb 1)

  • Presentation and submission instruction (Zheng)
  • Team lightning shares
  • Discussion and Q&A

Week 4 (Feb 8)

  • Project 1 presentations

Shortcuts: Shortcuts: Project 1 | Project 3

Project cycle 2: Physics-Informed Machine Learning

(starter codes)

Week 4 (Feb 8)

  • Project 2 starts.
  • Introduction to Project 2 (McKinley and Zheng)

Week 5 (Feb 15)

Week 6 (Feb 22)

Week 7 (Mar 1)

  • Discussion and Q&A

Week 8 (Mar 8)

  • Project 2 presentations

Shortcuts: Project 1 | Project 2

Project cycle 3: Predictive Modeling

(starter codes)

Week 9 (Mar 22)

  • Project 3 starts.
  • Climate Science Tutorial on "Air-Sea Flux of CO2" (McKinley)
  • Discussion and Q&A

Week 10 (Mar 29)

  • Tutorial on decision tree, random forests and xgboost (Zheng)
  • Review of starter codes (Xiaoshu Zhao)
  • Discussion of papers and Q&A

Week 11 (Apr 5)

  • Q&A on starter codes (Xiaoshu Zhao)
  • Discussion of research ideas

Week 12 (Apr 12)

  • Tutorials on explainable AI (Zheng)
  • Q&A on research ideas (McKinley)

Week 13 (Apr 19)

  • Guest lecture on LEAP's vision
  • Q&A

Week 14 (Apr 26)

  • Project 3 submission and presentations
Shortcuts: Shortcuts: Project 1 | Project 3

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A project-based learning course where teams of climate science and data science students collaborate to create machine learning predictive models for challenges inspired by LEAP's research

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  • Jupyter Notebook 98.2%
  • Python 1.8%