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ML Geo Curriculum

Machine Learning in the Geosciences

About this Book

Geosmart website
Acknowlegments
Primer

Chapter 1 - Open Source Ecosystem with Python

Getting Started
1.1 Open Reproducible Science
1.3 Jupyter Environment
1.3 Python Ecosystem
1.4 Computing Environments
1.5 Version Control & GitHub
1.6 Data Gallery

Chapter 2 - Data Manipulation

2.1 Data Definitions
2.2 Data Formats
2.3 Pandas
2.4 DataFrame Exploration
2.5 Data Arrays
2.6 Resampling Methods
2.7 Statistical Considerations for geoscientific Data and Noise
2.8 Spectral Transforms
2.9 Filtering Data
2.10 Synthetic noise
2.11 Feature Engineering
2.12 Dimensionality Reduction
2.13 ML-ready data
Assignment: Preparing AI-Ready Data for The Final Project

Chapter 3 - Machine Learning

3.1 Concepts in training supervision
3.2 Classification and Regression
3.3 Clustering: Unsupervied Classification
3.4 Binary classification
3.5 Multiclass Classification
3.6 Logistic regression
3.7 Random Forests
3.8 Robust Training
3.9 Ensemble learning
3.10 AutoML
3.20 Final Project - Classic Machine Learning
Homework Classic Machine Learning (50 points)

Chapter 4 - Deep Learning

4.0 The Perceptron
4.1 Neural Networks
4.2 Multi Layer Perceptrons
4.3 Model Training
4.2 Physics-Informed Neural Networks
4.3 Convolutional Neural Networks
4.4 Recurrent Neural Networks: Processing sequences
4.5 Auto-encoders
4.6 NAS: Network Architecture Search

Chapter 5 - Workflow Management and Reproducibility

This chapter focuces on model workflow and ML reproducibility

Chapter 6- Introduction to Cloud Computing

Browser Access to Cloud Instances
Terraform Access to Cloud Instances
Cloud Provider ML Jupyterhubs

Reference

Glossaries
Bibliography

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1.1 Open Reproducible Science

1.1 Open Reproducible Science#

We should strive to promote open and reproducible science in our work.

To that end, please review the following presentation:

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Getting Started

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1.3 Jupyter Environment

By eScience Institute, University of Washington
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