# Course - Uncertainty in Deep Learning MT25

> Source: https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/ · Updated: 2025-11-20 · Tags: uni, course

- [Course webpage](https://www.cs.ox.ac.uk/teaching/courses/2025-2026/UDL/) ([Moodle](https://courses.cs.ox.ac.uk/course/view.php?name=UDL_2025_2026))
- Lecture notes (old)
	- [1+2, Introduction and Bayesian probability theory](https://courses.cs.ox.ac.uk/pluginfile.php/14169/mod_resource/content/5/slides_w1.pdf)
	- [3+4, Bayesian probabilistic modelling](https://courses.cs.ox.ac.uk/pluginfile.php/14170/mod_resource/content/7/slides_w2.pdf)
	- [5+6, Uncertainty over functions and approximate inference](https://courses.cs.ox.ac.uk/pluginfile.php/14171/mod_resource/content/12/slides_w3.pdf)
	- [7+8, Mathematical tools and stochastic approximate inference](https://courses.cs.ox.ac.uk/pluginfile.php/14172/mod_resource/content/9/slides_w4.pdf)
	- [9+10, Classification in Bayesian neural networks and uncertainty in classification](https://courses.cs.ox.ac.uk/pluginfile.php/22812/mod_resource/content/10/slides_w6.pdf)
- Lecture videos
	- [2024-2025](https://ox.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22ad6decd1-4418-47c4-a60e-b1ea00efec40%22&view=1)
	- [2025-2026](https://ox.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22489152f3-ab1e-414b-863b-b36300955464%22)
- [Practicals](https://courses.cs.ox.ac.uk/pluginfile.php/26126/mod_resource/content/4/UDL_practicals.pdf) ([useful resources](https://courses.cs.ox.ac.uk/pluginfile.php/26127/mod_resource/content/2/useful_resources.pdf))
	- [Week 4, Setting a baseline](https://colab.research.google.com/drive/1E1835YbICRTr-tXco34S96OUWQqUNHic?usp=sharing)
	- [Week 5, A toolkit for the probabilistic machine learner](https://colab.research.google.com/drive/11hyKXJfkBcisx_frypQYjlKgbUhSybmi?usp=sharing)
	- [Week 6, Playing with uncertainties](https://colab.research.google.com/drive/1VXJgDP8wGm_ixsD8PYTI6A0kNj-fOFmN)
	- [Week 7&8, What does my model know?](https://colab.research.google.com/drive/1abII8K37PLD5LaUr53HowbYPCkflB2Y3)
- Additional references
	- [Bayesian epistemology](https://plato.stanford.edu/entries/epistemology-bayesian/)
	- [Gaussians recap](https://colab.research.google.com/drive/1oL7SOdjb3xHdf8ufXrnDHPCw-Psx0wgw)
	- [Oxford Applied and Theoretical Machine Learning Group](https://oatml.cs.ox.ac.uk/)
	- ["Probabilistic Machine Learning: An Introduction"](https://probml.github.io/pml-book/book1.html) by Kevin Murphy
	- ["Probabilistic Machine Learning: Advanced Topics"](https://probml.github.io/pml-book/book2.html) by Kevin Murphy
	- ["Uncertainty in Deep Learning"](https://www.cs.ox.ac.uk/people/yarin.gal/website/thesis/thesis.pdf), Prof. Yarin Gal's dissertation
- Other courses this term: [Courses MT25](https://ollybritton.com/notes/uni/part-a/mt25/)

### Notes
- [Notes - Uncertainty in Deep Learning MT25, Probability reference](https://ollybritton.com/notes/uni/prelims/mt22/probability/notes/probability-reference/)
- [Notes - Uncertainty in Deep Learning MT25, Statistics reference](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/notes/statistics-reference/)
- [Notes - Uncertainty in Deep Learning MT25, Kullback-Leibler divergence](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/notes/kullback-leibler-divergence/)

### Lectures
- [Lecture - Uncertainty in Deep Learning MT25, Introduction](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/introduction/)
- [Notes - Uncertainty in Deep Learning MT25, Bayesian probability theory](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/bayesian-probability-theory/)
- [Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/modelling/)
- [Lecture - Uncertainty in Deep Learning MT25, Bayesian probabilistic modelling of functions](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/functions/)
- [Lecture - Uncertainty in Deep Learning MT25, Uncertainty over functions](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/functions/)
- [Lecture - Uncertainty in Deep Learning MT25, Approximate inference](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/inference/)
- [Lecture - Uncertainty in Deep Learning MT25, Some very useful mathematical tools](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/tools/)
- [Lecture - Uncertainty in Deep Learning MT25, Stochastic approximate inference in DNNs](https://ollybritton.com/notes/uni/part-c/mt25/uncertainty-in-deep-learning/lectures/dnns/)
- [redacted](https://ollybritton.com/404)
- [redacted](https://ollybritton.com/404)

### Related Notes
- [Course - Probability](https://ollybritton.com/notes/uni/prelims/mt22/probability/)
- [Course - Machine Learning MT23](https://ollybritton.com/notes/uni/part-a/mt23/machine-learning/)
	- [Notes - Machine Learning MT23, Bayesian machine learning](https://ollybritton.com/notes/uni/part-a/mt23/machine-learning/notes/bayesian-machine-learning/)

### Problem Sheets

### To-Do List

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