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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
Powered by Jupyter Book
  • repository
  • open issue

Index

B | C | D | G | H | J | M | P | S

B

  • Binder
  • BinderHub

C

  • Conda

D

  • Docker

G

  • Git
  • GitHub

H

  • Hackweek

J

  • Jupyter Book
  • Jupyter Notebook
  • JupyterHub
  • JupyterLab

M

  • MyST

P

  • Project Jupyter

S

  • Slack

By eScience Institute, University of Washington
© Copyright 2022.