Processing and managing data

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signals • images • systems • sampling • colour • frequency • noise • artificial intelligence • regression • prediction • supervised and unsupervised learning • model selection and validation • geomatics • geographical reference systems • databases

Learning objectives

Master the basic tools for data analysis, processing and management, particularly with regard to:

  • acquisition, processing and analysis of signals and images
  • machine learning
  • databases and geographic information systems (GIS)


  • Basic knowledge of mathematics for the frequency representation of signals and systems (Fourier transform) and of probability and statistics.
  • Basic programming skills (CRAN R or Python, Matlab)

Brief description of the course

The introductory course in signal and image processing aims to introduce the concepts that serve as a basis for understanding the acquisition, processing and analysis of digital signals and images.

The introductory course in Machine Learning (in the sense of the capacity of an algorithm to extract knowledge from data) will develop the skills to define a machine learning pipeline, evaluate the quality of a predictive model, compare models and implement them using Python.

The introductory course on Geographic Information Systems (GIS) provides the theoretical basis for the use of GIS by presenting vector and raster models, OGC standards and spatial SQL in a simple manner.

The teaching is based on theoretical modules and application exercises.