a repo for material used at EJCP 2023 in Valence (July 2023)
The session was focusing on the reproducibility of results and empirical Software Engineering (SE). The first part was taken care of by Prof. Mathieu Acher (slides available at: https://inria.hal.science/hal-04152637) and this repo is about the second half of the day and proposes to focus on reproducibility and Machine Learning (ML).
To contribute to the x+y+z project (Mathieu Acher's practical session), you can pull request the following repo: https://github.com/FAMILIAR-project/reproducibility-associativity/
Slides that come back to the basics of ML are available in the Presentation folder. The animation for SVM is not there anymore and there are links at the bottom of slides that are clickable or URLs for reference. The TP folder contains material that was used during the practical session. Note that this practical session can be ran in multiple ways. It uses Python (3.xx) and Scikit-learn, a library that provides various different ML algorithms and implementations. We provide a jupyter notebook to run again in a controlled environment (so jupyter-lab and jupyter-notebook can be used). To make it even easier, jupyterLite https://jupyter.org/try-jupyter/lab/index.html is also available and run everything in the web. You just have to clone this repo and import the TP material in the jupyterLite session.
Make sure that the data (heart.csv) and the notebook are in the same folder or change the path to load the data in the notebook.
contacts: paul.temple@irisa.fr mathieu.acher@irisa.fr olivier.barais@irisa.fr