pyprobml is a collection of notebook-based implementations of probabilistic machine learning models and algorithms. It uses scientific computing and data analysis libraries to execute mathematical concepts and theories for practical application and research.
The project focuses on the programmatic generation of scientific figures and visualizations to recreate results from a technical text. It employs a system of branch-based asset storage to isolate these generated images from the source code.
The repository covers a wide range of probabilistic modeling and machine learning tasks, including Bayesian model prototyping, time series segmentation with hidden Markov models, and sampling via Gibbs sampling. It also includes educational tutorials for numerical computing and image classification.
The project utilizes automated notebook verification and execution testing to ensure code correctness and style consistency.