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Label noise in supervised and unsupervised classification

(Suitable for MSc thesis and MSc project)

Supervised classification methods require labeled data to train a classification model. In practice, however, training data typically contains a certain amount of wrongly labeled examples. In the domain of brain-computer interfaces, however, recently approaches for unsupervised classification has been proposed, which exploits specific domain knowledge in order to train a classification model from scratch (random initialization) without any labels, see

In this project, it shall be analyzed, how sensitive different supervised and unsupervised ML models are w.r.t. label noise.


  • solid machine learning background
  • solid math background (specifically linear algebra)
  • Fluent in python and with ML toolboxes
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