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Mini-courses syllabus

Mini-course 1: Machine Learning I applied to Remote Sensing Imagery

Dr. Franz Rottensteiner – Leibniz University Hannover

  1. Generative classifiers
  2. Logistic Regression
  3. SV and Randon Forest
  4. Graphical Models (Markov and Conditional Random Fields)

Mini-course 2: Photogrammetry applied to environmental studies

Dr. Anette Eltner – TU Dresden

1) Basic principles:
   – 3D reconstruction
   – Srea and feature based image matching
   – Structure from motion Photogrammetry
   – UAV systems and flight planning
   – Direct and indirect geo-referencing
2) Applications to environmental studies

 

Mini-course 3: Machine Learning II applied to Remote Sensing Imagery

Dr. Farid Melgani – University of Trento

  1. Artificial Neural Networks

Introduction. Artificial Neuron. Multilayer Perceptron. Radial Basis Function. Probabilistic Neural Network.

Regression with Neural Networks. Deep Learning. Autoencoder. Convolutional Neural Network.

  1. Support Vector Machines

Introduction. Kernel Representation. Generalization Theory. VC Dimension. Structural Risk Minimization.

Margin-Based Bounds. Generalization for Regression. Maximal Margin Classification. Soft Margin

Classification. Multiclass Support Vector Machines. Support Vector Regression.