1:00 - 2:00 PM

Introduction to Energy Machine Learning

Provide definitions, fundamental concepts of inference and prediction along with the opportunity and limitations of machine learning within the subsurface and other applications.

2:00 PM - 2:45 PM

Machine Learning Prediction with Naïve Bayes

Developing flexible predictors by building on Bayesian Statistics.

2:45 - 3:30 PM

Machine Learning Prediction with K-Nearest Neighbors

Motivation and methods for predictive machine learning methods including hyper parameter tuning with k nearest neighbors.

3:30 - 4:00 PM

Tree-based Machine Learning Prediction

Introduce tree-based modeling as one of the most interpretable machine learning prediction methods and as a prerequisite for more powerful ensemble methods. Segmenting the predictor space with variance/bias tradeoffs.
 

4:00 - 5:00 PM

Conclusion & Questions