Course - Uncertainty in Deep Learning MT25

• Course webpage (Moodle)
• Lecture notes (old)
  • 1+2, Introduction and Bayesian probability theory
  • 3+4, Bayesian probabilistic modelling
  • 5+6, Uncertainty over functions and approximate inference
  • 7+8, Mathematical tools and stochastic approximate inference
  • 9+10, Classification in Bayesian neural networks and uncertainty in classification
• Lecture videos
  • 2024-2025
  • 2025-2026
• Practicals (useful resources)
  • Week 4, Setting a baseline
  • Week 5, A toolkit for the probabilistic machine learner
  • Week 6, Playing with uncertainties
  • Week 7&8, What does my model know?
• Additional references
  • Bayesian epistemology
  • Gaussians recap
  • Oxford Applied and Theoretical Machine Learning Group
  • “Probabilistic Machine Learning: An Introduction” by Kevin Murphy
  • “Probabilistic Machine Learning: Advanced Topics” by Kevin Murphy
  • “Uncertainty in Deep Learning”, Prof. Yarin Gal’s dissertation
• Other courses this term: Courses MT25^U

Notes

• Notes - Uncertainty in Deep Learning MT25, Probability reference^U
• Notes - Uncertainty in Deep Learning MT25, Statistics reference^U
• Notes - Uncertainty in Deep Learning MT25, Kullback-Leibler divergence^U

Lectures

• Lecture - Uncertainty in Deep Learning MT25, Introduction^U
• Notes - Uncertainty in Deep Learning MT25, Bayesian probability theory^U
• Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling^U
• Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling of functions^U
• Lecture - Uncertainty in Deep Learning MT25, Uncertainty over functions^U
• Lecture - Uncertainty in Deep Learning MT25, Approximate inference^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
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Related Notes

• Course - Probability^U
• Course - Machine Learning MT23^U
  • Notes - Machine Learning MT23, Bayesian machine learning^U

Problem Sheets