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Principles of spatial data mining and machine learning


NFDI4Earth

About This Course

If you're interested in machine learning and want to learn how to apply it to real-world scenarios, this course is for you. It focuses on hands-on assignments using Python's sklearn, pandas, and matplotlib libraries and is structured around four key areas. You'll start by learning about linear and polynomial regression models, data splitting, and evaluation metrics. Then you'll move on to logistic regression and explore classification models and accuracy assessment. The course also covers clustering techniques, where you'll experiment with KMeans, K-Medoids, and DBSCAN models. Finally, you'll master classification strategies through the implementation of Support Vector Machine models, exploring various kernels. This course is designed for learners who want to apply theoretical concepts in practical situations and gain a solid foundation in machine learning methodologies (Access this course on GitLab).

Level

Intermediate, Advanced

Requirements

Python onboarding

Subject Area

Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography

What You Will Learn

  • Linear and polynomial regression models, data splitting, and evaluation metrics
  • Logistic regression, classification models, and accuracy assessment
  • Clustering techniques with KMeans, K-Medoids, and DBSCAN models
  • Classification strategies with Support Vector Machine models, exploring various kernels

Resources

Coding4Geo by Yu Feng, Xin Wang, Hao Li

Administration

Farzaneh Sadeghi

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