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Machine Learning for Geospatial Sciences
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Learning outcomes
Explain ML algorithms covered in the course (e.g., clustering, classification, regression)
Perform feature engineering to choose, manipulate, and transform geospatial raster and vector data into features that can be used in a ML algorithm
Interpret the prediction results and generalisation capability of the applied machine learning model
Apply machine learning algorithms introduced in this course for different tasks in support of geospatial applications
Perform exploratory data analysis to enhance the understanding of available data
Adequately partition a labeled data set for training, hyper-parameter tuning, and accuracy assessment
Select appropriate datasets to address geospatial related applications using machine learning algorithms
UNIVERSITY OF TWENTE
Faculty of Geo-Information Science and Earth Observation
GeoCourseHub at Utwente.nl