Applied Machine Learning for Spatial Analysis

Course Description

Machine Learning (ML) is a branch of artificial intelligence that enables computer systems to learn from data and improve their predictive capacity without explicit human intervention. ML algorithms are growingly being applied to solve complex spatial problems due to their ability to efficiently process large, high-dimensional datasets, improve predictive capacity and reveal hidden patterns in data. This course provides conceptual foundations, methods and analytical skills for the application of broadly used machine learning methods to geospatial analysis. The course will enable students to become familiar with basic definitions, conceptual foundations and applications of different machine learning methods. Students will become familiar with the main steps related to the conception, development, evaluation and interpretation of predictive models using machine learning methods. Students will also learn basic computer programming skills for spatial data integration and their use for the calibration and evaluation of spatial machine learning models. Students who took GUS 5074: Applied Machine Learning for Spatial Analysis may not take this course for additional credit.

Code

GUS 5034

Credits

3

Subject

GUS

Programs That Include This Course

• Graduate Certificate Graduate Certificate in Geospatial Data Science