ML_tutorials_geodesy

A set of tutorial examples showcasing standard machine learning algorithms and toy examples from geodesy. The code was created jointly by Jemil Butt and Zan Gojcic ( both ETH Zurich, Institute of geodesy and photogrammetry) for the purpose of illustrating standard processing pipelines encountered in machine learning in front of a geodetic background. Originally, the code was used to supplement a tutorial on machine learning methods hosted by TU Munich during the Ingenieurvermessungskurs in 2020. There are tutorials on

1. Supervised learning: Regression
2. Supervised learning: Classification
3. Unsupervised learning: Clustering
4. Unsupervised learning: Dimensionality reduction
5. Unsupervised learning: Independent component analysis
6. Methods: Training of neural networks