MATH-804 / 2 crédits

Enseignant: Davison Anthony Christopher

Langue: Anglais

Remark: Registration via website


Only this year


ML for predictive modeling is important in both industry and research. We join experts from stats and math to shed light on particular aspects of the theory and interpretability of DL. We discuss the statistical theory and generalization behavior of deep NNs, and how to move towards trustworthy DL.



statistics, statistical learning, optimization, deep learning, regression, interpretability

Dans les plans d'études

  • Forme de l'examen: Rapport de TP (session libre)
  • Matière examinée: MLSTATS
  • Cours: 16 Heure(s)
  • TP: 16 Heure(s)

Semaine de référence