CVEN-5833

AI in Earth System Science and Engineering

University of Colorado Boulder

Course Overview

Course Information

  • Course Number: CVEN-5833
  • Course Title: AI in Earth System Science and Engineering
  • Credits: 3
  • Term: Spring 2026

Meeting Times

  • Days: TTH
  • Time: 8:30-9:45
  • Location: SEEC N124

Course Description

This course explores the application of artificial intelligence and machine learning techniques to problems in Earth System Science. Students will learn how modern AI methods can be applied to analyze, model, and predict complex Earth system processes including climate dynamics, hydrology, atmospheric science, and environmental monitoring.

Learning Objectives

Upon successful completion of this course, students will be able to:

Instructor Information

Instructor

Teaching Assistant

  • Name: TBD
  • Email: TBD
  • Office Hours: TBD

Prerequisites

  • Linear Algebra
  • Calculus
  • Programming (Python)

    • Proposed Course Topics

    No. Main Topic Items
    1 Introduction to Programming Language and AI & Earth System Data
  • Overview of the field
  • Earth System Data
  • Programming Language
    		•
    2 Supervised Learning and Regression
  • Definition of the supervised learning setup
  • Weighted Least Squares
    		•
    3 Classification and Non-Linearity
  • Logistic Regression
  • Kernels
    		•
    4 Model-Based and Non-Parametric Methods
  • Support Vector Machines (SVM)
  • Tree-Based Methods: Decision Trees and Random Forests
    		•
    5 Deep Learning Fundamentals
  • Artificial Neural Networks (ANNs) and activation functions
  • Backpropagation and gradient descent
    		•
    6 Convolutional Neural Networks (CNNs) for Spatial Data
  • CNN Architecture
  • Advanced techniques: The U-Net
  • Transfer Learning and data augmentation
    		•
    7 Recurrent and Sequence Models
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM)
    		•
    8 Graph and Generative Models
  • Graph Neural Networks (GNNs)
  • Generative AI: Introduction to Generative Adversarial Networks (GANs) and Diffusion Models
  • Applications of generative models
    		•
    9 Advanced Sequence Modeling
  • Transformer Architecture
  • Attention Mechanism
  • Applications of transformers
    		•
    10 Foundation Models in Practice
  • Geospatial and Weather Foundation Models
  • large models for zero-shot/few-shot learning, fine tuning
    		•
    11 Logistic Regression & Kernels [Assignment]
    12 Societal Impact & Ethics
  • Integrated case studies: Natural Hazard Risk Quantification
  • AI for Sustainability, water resource management
  • Ethics of AI in Earth Science
    		•

    Assignments

    Assignment 1: Data Analysis

    Due Date: TBD

    Assignment 2: TBD

    Due Date: TBD

    TBD

    Assignment 3: TBD

    Due Date: TBD

    TBD

    Final Project

    Due Date: [Date]

    Comprehensive project applying AI methods to an Earth system science problem...

    Grading Policy

    Component Weight
    Assignments 40%
    Midterm Exam 25%
    Final Project 30%
    Participation 5%

    Grading Scale

    • A: 90-100%
    • B: 80-89%
    • C: 70-79%
    • D: 60-69%
    • F: <60%

    Course Policies

    Late Submissions

    Assignments submitted after the due date will be penalized 10% per day late, up to a maximum of 3 days. After 3 days, assignments will not be accepted without prior approval from the instructor.

    Academic Integrity

    All work submitted must be your own. Collaboration on assignments is allowed up to the point of sharing code or solutions. Any violation of academic integrity will be reported to the Honor Code Council.

    Accommodations

    Students with disabilities who need accommodations should contact Disability Services and inform the instructor as early as possible in the semester.

    Resources

    Online books

    Youtube videos

    Software/Tools

    Statistics