Machine Learning for Earth and Energy Systems

Catalog description

This course provides an overview of the application of machine learning algorithms to problems in earth and energy system sciences and engineering. The course addresses the strengths and weaknesses of various machine learning approaches, as well as appropriate testing and validation techniques for these complex models. Topics include machine learning applications in regression and classification, ranging from linear regression to neural networks. The course focuses on the practical application and interpretation of statistical machine learning methods, while also providing an understanding of the basis and theory behind these methods. An emphasis of this course is for students to apply these methods to specific research problems of interest. Additionally, this course teaches a modern programming language (e.g., R). Students with some background in statistics, but no previous formal training in machine learning algorithms will find this course most useful. Students do not need to have prior experience with a specific programming language to enroll in this course, however, some previous training in statistics or programming will be beneficial.