Course - Uncertainty in Deep Learning MT25

Created: October 09, 2025 | Updated: October 22, 2025 | About these notes | View in context | Study these flashcards

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• 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
• 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

Lectures

• [[Lecture - Uncertainty in Deep Learning MT25, I]]^?

Related Notes

• [[Course - Probability MT22]]^U
• [[Course - Machine Learning MT23]]^U
  • [[Notes - Machine Learning MT23, Bayesian machine learning]]^U

Problem Sheets

To-Do List

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