GIS 488KVT: Current Topics in GIS

Catalog

Prerequisite(s): GIS 2250 with a grade of "C-" or better Students are introduced to fundamental concepts of machine learning and artificial intelligence (AI) relevant to GIS and Earth science. Students learn how to apply machine learning and AI models to analyze Earth observation data, develop predictive models, evaluate model performance, and interpret results in a scientific context. Students also examine the societal and ethical impacts of AI techniques in GIS. Topics include supervised, unsupervised, and semi-supervised learning; regression and classification; deep learning fundamentals; convolutional neural networks; recurrent neural networks; graph models; generative models; and social impact and ethics. Students learn how to implement these machine learning and AI models using an online Python coding platform.