Welcome to Hands-on Machine Learning for Earth and Environmental Sciences

Fall 2024 edition

Introduction

• Running Python scripts

(Part I) Basics of Scientific Programming for Applied Machine Learning

• 1. Introduction to Python for Earth and Environmental Sciences

(Part II) Basics of Machine Learning for Earth and Environmental Sciences

• 2. Linear Regression for Regression, Logistic Regression for Classification and Statistical Forecasting
• 3. Supervised Learning (Decision Trees, Random Forests, Support Vector Machines) and Environmental Risk Analysis
• 4. Unsupervised Learning (Clustering, Dimensionality Reduction) and Environmental Complexity

(Part III) Deep Learning for the Geosciences

• 5. Artificial Neural Networks and Surrogate Modeling
• 6. Convolutional Neural Networks and Remote Sensing
• 7. Recurrent Neural Networks and Hydrological Modeling
• 8. Graph Neural Networks and Interconnected Systems

(Part IV) Towards Trustworthy AI

• 9. Explainable Artifical Intelligence and Understanding Predictions
• 10. Generative Modeling and Uncertainty Quantification
• 11. Hybrid Modeling and Knowledge-Guided Learning