This course focuses on the principles of learning from data and quantification of uncertainty, by complementing and enriching the Introduction to Statistical Learning course. The presentation of the material follows a common thread based on the probabilistic data modeling approach, so that many classical algorithms, such as least squares and k-means, can be seen as special cases of inference problems for more general probabilistic models. Taking a probabilistic view also allows the course to derive inference algorithms for a class of nonparametric models that have close connections with neural networks and support vector machines. This advanced course is complemented by these lab sessions to guide students through the design and validation of the methods developed during the lectures.
Google Colab is the suggested way to edit and execute the labs. Simply click on the icon to open the corresponding notebook in Colab.
| Week 0 | Introduction to Python/Numpy |  |
| Week 1 | Bayesian linear regression | |
| Coin toss experiment | |
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| Week 2 | Gaussian process regression | |
| Week 3 | Logistic regression with MCMC | |
| Week 4 | Variational inference | |
Note: choose this option only if you know what you are doing. You can clone the development environment using Conda (installation guide here), by running
> conda env create --file=environment.ymlOnce the installation has completed, you can activate the new environment with
> conda activate asiFinally launch Jupyter Lab with
> jupyter lab