Short Course
SC-09: Practical Machine Learning Methods in the Geosciences
Sponsored By the (SEG) Society of Exploration Geophysists
Monday, 26 August
8:00 a.m.–5:00 p.m.
George R. Brown Convention Center, Level 3, Room 352D
Learn the high-level principles of five important topics in machine learning: neural networks, convolutional neural networks, support vector machines, principal component analysis, and clustering methods. Practical examples in geosciences will be used to show the applications of each method. Practice the execution of these methods on MATLAB and Keras codes. The teaching format is 50-minute lectures and 1-hour labs to reinforce the principles of each method.
Course Leader(s)
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 one day 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.
Attendees of this course 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 problems.
- Understand the limitations and benefits of each MLmethod.
- 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 problems.
- Understand the limitations and benefits of each MLmethod.
Fee:
$850 Professional Member
$1,000 Professional Nonmember
$150 Student
$1,000 Professional Nonmember
$150 Student
30
CEU: .8