Skip to main content

Ctrl+K

Introduction

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

(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
- 8.1. (Exercise) Graph Neural Networks with PyTorch

(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
- 11.1. (Exercise) Introduction to Hybrid models

Index

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