[2006.13311] 70 years of machine learning in geoscience in reviewopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging towards a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades. Furthermore, this chapter explores the shift from mathematical fundamentals and knowledge in software development towards skills in model validation, applied statistics, and integrated subject matter expertise. The review is interspersed with code examples to complement the theoretical foundations and illustrate model validation and machine learning explainability for science. The scope of this review includes various shallow machine learning methods, e.g. Decision Trees, Random Forests, Support-Vector Ma

3 mentions: @JesperDramsch@momiji_fullmoon@_arXiv_physics
Date: 2020/06/30 09:51

Referring Tweets

@JesperDramsch Ever wonder where this #machinelearning thing in #geoscience is coming from? Why is #deeplearning suddenly a thing? Does #ai or #ml have any history here? Have I got a #preprint for you: "70 years of machine learning in geoscience in review" t.co/EwVZ6SvoHJ t.co/iN5LIL2ecZ

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