Machine Learning for Geospatial Sciences
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 algorithmInterpret the prediction results and generalisation capability of the applied machine learning modelApply 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 dataAdequately partition a labeled data set for training, hyper-parameter tuning, and accuracy assessmentSelect appropriate datasets to address geospatial related applications using machine learning algorithms
is course learning outcome of
Machine Learning for Geospatial Sciences
EC credits
durationInHoursFromECTS
durationInWeeks
hasCourseLearningOutcome
has delivery mode
has learning outcome
hasLearningUnit
hasPrerequisiteComment
Statistics, calculus, linear algebra, analytics geometry, programming (Python)
@en
isPartOfLearningPathway
is part of programme
is part of specialisation
number of staff
quartileAlternativeITC
abstract
CONCEPT (not yet confirmed by ...... niques to geospatial sciences.
@en
title
Machine Learning for Geospatial Sciences
@en
label
Machine Learning for Geospatial Sciences
@en
prefLabel
Machine Learning for Geospatial Sciences
@en