This repository is used to host the files needed for the exercise sessions and the computer lab in the course Advanced Probabilistic Machine Learning at Uppsala University.
The material associated with each session listed below is given together with a set of recommended problems.
For each session, the material consists of the following:
- Jupyter notebook with problems.
- Direct link to run notebook with Binder.
- Direct link to run notebook with Google Colab.
- Notebook exported to HTML with solutions.
Data used in the computer classes can be downloaded directly in the notebooks. For offline use, we recommend you download the whole repository and make the necessary changes to the notebook by commenting/uncommenting appropriate lines.
| Topic | Course | Recommended | Extra | Files | |
|---|---|---|---|---|---|
| 3 | Bayesian linear regression | 1RT003, 1RT705 | 1, 2 | 3 |     |
| 8 | Gaussian Processes: Part 1 | 1RT003 | 1, 2, 3 | 4 |   |
| 9 | Gaussian Processes: Part 2 | 1RT003 | 1, 2 | 3, 4 |   |
| 11 | Unsupervised learning | 1RT003, 1RT705 | 1, 2 | 3 |   |
For the computer lab about unsupervised learning the following resources are available:
| Topic | File | Links |
|---|---|---|
| Lab instructions | instructions.pdf |  |
| Introduction to PyTorch | introduction.ipynb |  |
| Probabilistic PCA | PPCA.ipynb |  |
| Variational autoencoder | VAE.ipynb |  |
| Deep hierarchical VAE | NVAE.ipynb |  |