Uncertainty Quantification of Machine Learning Models

Laurens Sluijterman • Boek • paperback

• Samenvatting
  Free download at https://doi.org/10.54195/9789465150475
  
  Machine learning models have become significantly more popular in recent years and are increasingly being used in areas where reliability is crucial. Think, for example, of self-driving cars or analyzing CT scans. To have confidence in a model, it is necessary to quantify its uncertainty. Since machine learning models differ from classical models in crucial ways – they typically have more parameters than data points and are slightly different each time they are created – classical techniques for quantifying uncertainty cannot be directly applied. This work provides new contributions to address this problem.
• Productinformatie
  Binding : Paperback
  Distributievorm : Boek (print, druk)
  Formaat : 170mm x 240mm
  Aantal pagina's : 225
  Uitgeverij : Radboud University Press
  ISBN : 9789465150475
  Datum publicatie : 03-2025
  
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