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This course covers big-picture machine learning buzz words with both humor and unassailable frankness. The goal of the course is for every geoscientist to gain confidence in these important concepts and how they add to our well-established practices, particularly seismic interpretation. Presentation topics include a machine learning historical perspective, what makes it different, a fish factory, Shazam, comparison of supervised and unsupervised machine learning methods with examples, tuning thickness, deep learning, hard/soft attribute spaces, seismic wavelets and multi-attribute samples, and several interpretation examples. On conclusion, you may not know how to run machine learning algorithms, but you should be able to appreciate their value and some of their limitations.
- Historical perspective on machine learning
- Example of ML self-adaptation
- What makes machine learning different
- Supervised and unsupervised ML
- Deep learning example
- Tuning thickness
- ML terminology and what it means
- Seismic examples of supervised and unsupervised ML
- Geobodies and Prediction