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.
Prerequisites (Knowledge/Experience/Education required)
BS degree. Matrices, Vectors, Matrix Inverse, Calculus, Partial Derivatives
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.
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.