Chapter Overview#

Chapter 3: Classic Machine Learning in Geosciences#

This chapter explores the foundational concepts and techniques of Classic Machine Learning (CML) relevant to Geoscience. Classic Machine Learning is often faster to develop and implement, making it an excellent opportunity to establish new concepts and best practices for machine learning. This chapter covers a wide range of topics, from basic principles to advanced techniques, providing a comprehensive understanding of machine learning.

Topics Covered#

  1. Machine Learning Concepts

    • Differences between supervised and unsupervised learning and relevance to geosciences

    • Key terminology and definitions

  2. Supervised Learning

    • Classification vs. Regression

      • Understanding the differences and applications

    • Multiclass Classification

      • Techniques and strategies for handling multiple classes

    • Ensemble Learning

      • Combining multiple models to improve performance

      • Techniques such as bagging, boosting, and stacking

    • Random Forests

  3. Unsupervised Learning

    • Clustering

      • Techniques such as K-means, hierarchical clustering, and DBSCAN

    • Dimensionality Reduction

      • Techniques such as PCA and t-SNE

  4. Training Strategies

    • Cross-Validation

      • Techniques for model validation and selection

    • Hyperparameter Tuning

      • Methods for optimizing model performance

  5. Generalization and Robustness

    • Best Practices for Robust ML

      • Techniques to prevent overfitting and improve generalization

    • Model Evaluation

      • Metrics and methods for assessing model performance

  6. Auto-ML

    • Concepts of auto-machine learning

    • implementation using pycaret

Learning Outcomes#

By the end of this chapter, you will:

  • Gain a solid understanding of the fundamental concepts and techniques of classic machine learning.

  • Learn how to build, train, and evaluate machine learning models using popular frameworks.

  • Explore advanced topics and best practices for robust learning and generalization.

  • Apply machine learning techniques to solve real-world problems and analyze case studies.

Assignments#

  • Homework: There is one homework assignment to reinforce the concepts learned in this chapter.

  • Final Project Milestone: There is one final project milestone guideline to help you apply the concepts to a comprehensive project.

We hope this chapter provides you with a thorough understanding of classic machine learning and inspires you to explore its vast potential in various fields.