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 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
- [[Lecture - 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
- [[Notes - Uncertainty in Deep Learning MT25, Classification in Bayesian neural networks]]?
- [[Notes - Uncertainty in Deep Learning MT25, Uncertainty in classification]]?