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MGEO Redesign
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hasSubject
Machine Learning for Geospatial Sciences
General information
Learning outcomes
Structure
Topics
Topics
geospatial vector data
regression
correlation
machine learning model
geospatial science
available data understanding
exploratory data analysis
least squares
geospatial applications
feature transformation
features manipulation
training
regularisation
K-means
backpropagation
perceptron
logistic regression
Feature spaces
accuracy assessment
prediction results
generalisation capability
labeled dataset
MLP
neural network
features selection
K-NN
spatio-temporal data
Initialization
Universal approximation theorem
geospatial raster data
feature extraction
hyper-parameter tuning
Self-organised maps
feature engineering
dataset
clustering
machine learning algorithms
loss function
classification
activation functions
classes separability
linear regression
Feature selection
features
geodata
images
features transformation
Machine learning algorithm
Gradient descent
non-linear dependance
feauture engineering
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0c63e6b1-9752-4b62-a942-192cbef65315
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UNIVERSITY OF TWENTE
Faculty of Geo-Information Science and Earth Observation
GeoCourseHub at Utwente.nl