Course - Uncertainty in Deep Learning MT25
- Course webpage (Moodle)
- Lecture notes (old)
- Lecture videos
- Practicals (useful resources)
- 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 MT25U
Notes
- Notes - Uncertainty in Deep Learning MT25, Probability referenceU
- Notes - Uncertainty in Deep Learning MT25, Statistics referenceU
- Notes - Uncertainty in Deep Learning MT25, Kullback-Leibler divergenceU
Lectures
- Lecture - Uncertainty in Deep Learning MT25, IntroductionU
- Notes - Uncertainty in Deep Learning MT25, Bayesian probability theoryU
- Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modellingU
- Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling of functionsU
- Lecture - Uncertainty in Deep Learning MT25, Uncertainty over functionsU
- Lecture - Uncertainty in Deep Learning MT25, Approximate inferenceU
- Lecture - Uncertainty in Deep Learning MT25, Some very useful mathematical toolsU
- Lecture - Uncertainty in Deep Learning MT25, Stochastic approximate inference in DNNsU
- redacted?
- redacted?