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[In the last several decades, computer scientists and statisticians have developed and implemented a plethora of machine learning (ML) algorithms. Although the application of data-driven modeling is relatively new to geoscience, we can trace back some of its early applications to the 1980’s and 1990’s. This chapter will discuss the fundamental theory and analytic framework of many popular ML algorithms. Understanding the fundamentals of these algorithms, network-specific hyperparameters, and their meaning is essential to better implement these algorithms in our datasets and enhance the success rate of data-driven modeling. These algorithms are based on solid mathematical and statistical theories. Indeed, some algorithms are better than others for certain types of applications; however, sometimes, our lack of understanding of algorithms and the nuances of their applications to specific datasets cause them to underperform compared to others. Once we understand the fundamentals of algorithms and our datasets, ML will be more fun and provoking, which will facilitate further progress of geo-data science.]
Published: May 4, 2021
Keywords: Machine learning algorithms; Model hyperparameters; Clustering; Neural networks; Decision trees; Deep learning; Ensemble learning; Physics-informed machine learning
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