Course Lecturers: Mauricio A Álvarez, Michele Caprio, and Omar Rivasplata
Machine learning is increasingly being used for decision support in data-driven applications. A key concept when making decisions based on predictive models is that of uncertainty, e.g., in applications of AI where safety or trustworthiness are required. Uncertainty quantification recognises that exact predictions are often out of reach due to theoretical or practical limitations.
This module studies different probabilistic machine-learning models that incorporate uncertain reasoning and the mathematical concepts and algorithms required to learn such models from data.
You can run the Jupyter Notebooks directly on Google Colab. Click on each Colab Badge to open the notebook.
| Session date | Lab session | Google Colab link |
|---|---|---|
| October 4 | Probability & Statistics |  |
| October 18 | Inference | |
| November 8 | Probabilistic Graphical Models | |
| November 22 | Bayesian Neural Networks | |
| December 6 | Introduction to Gaussian Processes | |
If you want to save changes to the Notebook, you need to save them before quitting. According to this link:
If you would like to save your changes from within Colab, you can use the File menu to save the modified notebook either to Google Drive or back to GitHub. Choose File→Save a copy in Drive or File→Save a copy to GitHub and follow the resulting prompts. To save a Colab notebook to GitHub requires giving Colab permission to push the commit to your repository.