Diligent students will:

• Learn how to apply ML methods to geoscience examples.
• Understand key principles underlying each of the ML methods.
• Practice manipulating MATLAB and Keras ML codes so they can adapt the codes to their own problems.
• Understand limitations and benefits of each ML method.

• Learn the high-level principles of five important topics in machine learning: neural networks; convolutional neural networks; support vector machines; principal component analysis; clustering methods. Practical examples in geosciences will be used to show applications of each method.
• Practice the execution of these methods on MATLAB and Keras codes.
• Teaching format is 50 minute lectures and 1-hour labs to reinforce principles of each method.
• The short course is for physical scientists who have heard about ML and might know some details, but lack enough knowledge to assess ML applications in their specialty.
• This limitation will be eliminated after two days of diligent attendance.
• A selective overview of important ML topics is provided and their practical understanding comes from MATLAB exercises.
• Machine learning examples are taken from the fields of astronomy, medicine, geosciences, and material sciences.
• All participants will learn how to use ChatGPT or Deepseek to produce useful ML or Javascript codes that solve practical geophysical problems.

Diligent students will:

• Regression: Predicting continuous variable values from known data points (e.g., estimating reservoir properties).
• Classification: Determining discrete variable categories from observed data (e.g., lithology identification).
• Clustering: Automatically grouping datasets into distinct classes without prior labels (e.g., seismic facies segmentation).

• Linear & Logistic Regression.
• Multilayer Perceptrons (Neural Networks).
• Kohonen Self-Organizing Maps.
• k-Nearest Neighbors (k-NN).