Carbon footprint aware Machine Learning hos Københavns Universitet

Machine learning

I sektionen arbejdes der med at analysere de eksplosivt voksende datamængder i vores samfund og udvikle de metoder, som gør computere i stand til at lære fra data, f.eks. Kunstig intelligens træning af computere ud fra data.

Forskningsfeltet opdeles på DIKU i fire hovedområder

  • Machine Learning Theory
  • Medical Imaging
  • Information Retrieval
  • Machine Learning in Biology

 

Læs mere her: https://di.ku.dk/english/research/machine-learning/

 

 

Engelsk version:

 

The activities in the section range from research into the theoretical foundations of machine learning to applications within a broad set of domains, including remote sensing, information retrieval, medical image analysis and modelling of biological data.

 

The research section is divided into four areas:

 

  • Machine Learning Theory
  • Medical Imaging
  • Information Retrieval
  • Machine Learning in Biology

 

Read more here: https://di.ku.dk/english/research/machine-learning/

 

 

 

Project idears:

  • Want to study ML from a sustainability point of view?
  • Interested for Network Architecture Search?

Supervisor:
Raghav Selvan, raghav@di.ku.dk

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