Skip to main content
Ctrl+K
Logo image
  • Welcome to Hands-on Machine Learning for Earth and Environmental Sciences

Introduction

  • Running Python scripts

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

  • 1. Introduction to Python for Earth and Environmental Sciences
    • 1.1. Variables, Control Flow, and File I/O
    • 1.2. (Exercises) Text and Tabular Files
    • 1.3. Data Structure, Functions, and Classes
    • 1.4. (Exercises) Simple Data Structures
    • 1.5. Scientific Computing with Numpy
    • 1.6. (Exercise) Ocean Floats Data Analysis
    • 1.7. Visualization with Matplotlib and Cartopy
    • 1.8. (Exercises) Replicating plots
    • 1.9. Tabular Data with Pandas
    • 1.10. (Exercise) Earthquake Data Analysis
    • 1.11. Geospatial Data with Geopandas
    • 1.12. (Exercise) Hurricane Track Analysis
    • 1.13. Regression, Classification, and Clustering with Scikit-learn
    • 1.14. (Exercises) Multivariate linear regression and clustering
    • 1.15. Statistical Graphics with Seaborn
    • 1.16. (Exercise) Marathon Data Analysis

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

  • 2. Linear Regression for Regression, Logistic Regression for Classification and Statistical Forecasting
    • 2.1. Classification and Regression
    • 2.2. (Exercises) Classification
    • 2.3. (Exercises) Training Models
    • 2.4. Statistical Forecasting in Environmental Sciences
    • 2.5. (Exercises) Statistical Forecasting
  • 3. Supervised Learning (Decision Trees, Random Forests, Support Vector Machines) and Environmental Risk Analysis
    • 3.1. Simple Machine Learning Algorithms for Classification Tasks
    • 3.2. (Exercises) Support Vector Machines
    • 3.3. (Exercises) Decision Trees and Random Forest
    • 3.4. (Exercises) Ensemble Modeling and Stacking
    • 3.5. (Exercises) Wildfire Susceptibility Mapping
  • 4. Unsupervised Learning (Clustering, Dimensionality Reduction) and Environmental Complexity
    • 4.1. Unsupervised Learning for Clustering and Dimensionality Reduction
    • 4.2. (Exercise) Dimensionality Reduction
    • 4.3. (Exercise) Clustering
    • 4.4. (Exercise) Ocean Regimes Identification

(Part III) Deep Learning for the Geosciences

  • 5. Artificial Neural Networks and Surrogate Modeling
    • 5.1. Introduction to Artificial Neural Networks
    • 5.2. (Exercise) Artificial Neural Networks with Keras
    • 5.3. (Exercise) Physically-Informed Climate Modeling
  • 6. Convolutional Neural Networks and Remote Sensing
    • 6.1. Convolutional Neural Networks and Remote Sensing
    • 6.2. (Exercise) Deep Computer Vision
    • 6.3. (Exercise) Land Cover Classification
  • 7. Recurrent Neural Networks and Hydrological Modeling
    • 7.1. Introduction to Recurrent Neural Networks
    • 7.2. Neural Networks for Time Series Predictions
    • 7.3. (Exercise) Composing Music
    • 7.4. (Exercise) Hydrological Modeling
  • 8. Graph Neural Networks and Interconnected Systems
    • 8.1. (Exercises) Graph neural networks with PyTorch
    • 8.2. (Exercise) Neural Weather Prediction

(Part IV) Towards Trustworthy AI

  • 9. Explainable Artifical Intelligence and Understanding Predictions
    • 9.1. Why do we need machine learning model interpretability?
    • 9.2. (Exercise) XAI on Simple Datasets
  • 10. Generative Modeling and Uncertainty Quantification
    • 10.1. (Exercise) Introduction to Uncertainty Quantification and Generative Modeling
    • 10.2. (Exercise) Autoencoders, Generative Adversarial Networks, and Diffusion Models
  • 11. Hybrid Modeling and Knowledge-Guided Learning
    • 11.5. (Exercise) Introduction to Hybrid models
  • Repository
  • Open issue
  • .md

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

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

next

First Time Coding in Python?

By Tom Beucler, Milton Gomez, Frederick Iat-Hin Tam, Jingyan Yu, Saranya Ganesh S, Haokun Liu, Kejdi LLeshi, Ayoub Fatihi

© Copyright 2022.