ob
  • about
  • blog
  • notes (current)
  • misc
Notes University Notes Part C Courses MT25 Uncertainty in Deep Learning Lectures 7 Bayesian probability theory Uncertainty in Deep Learning MT25, Approximate inference Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling Uncertainty in Deep Learning MT25, Introduction Uncertainty in Deep Learning MT25, Some very useful mathematical tools Uncertainty in Deep Learning MT25, Stochastic approximate inference in DNNs Uncertainty in Deep Learning MT25, Uncertainty over functions Kullback-Leibler divergence Statistics reference

Lectures

Created: October 31, 2025 | Updated: October 31, 2025 | About these notes


  • [[Lecture - Uncertainty in Deep Learning MT25, Approximate inference]]U
  • [[Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling]]U
  • [[Lecture - Uncertainty in Deep Learning MT25, Introduction]]U
  • [[Lecture - Uncertainty in Deep Learning MT25, Some very useful mathematical tools]]U
  • [[Lecture - Uncertainty in Deep Learning MT25, Stochastic approximate inference in DNNs]]U
  • [[Lecture - Uncertainty in Deep Learning MT25, Uncertainty over functions]]U
  • [[Notes - Uncertainty in Deep Learning MT25, Bayesian probability theory]]U
© Copyright 2026 Olly Britton. Last updated: May 16, 2026.