Multiple different atmospheric science fields have benefitted from the rapid expansion of machine learning and deep learning model capabilities within the past decade. Differing from traditional prediction problems, meteorological tasks require specific data pre-processing and model architecture needs. This tutorial uses numerical weather prediction model output to describe fundamental machine learning principles for both traditional modeling (such as random forests, logistic regression, etc.) and deep learning. Fundamental principles include data-preprocessing, feature selection, in addition to model training, tuning, and evaluation. The hands-on lecture will explore linear models as well as neural networks, with resources on other modeling types.
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