Machine Learning & Computational Thinking for Cirkus Naturligvis 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 idea:

  • Help design a series of activities that inspire kids (grade 4-10) to explore computational thinking with machine learning, AI, and robotics
  • Activities can include social robotics, smart noses, data logging sensors for the environment. Also how to use programming to support Science, Technology, Engineering, Art, and Math (STEM)
  • Work with https://science.ku.dk/oplevscience/grundskolen/paa-skolen-cirkusnaturlig


 

Supervisor: 

Daniel Spikol, ds@di.ku.dk

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