In this session, we will introduce the taxonomy of unsupervised ML methods, with a focus on clustering algorithms. We will present a real-case scenario, based on a RL pipeline developed by VO at the NCR. Python code (but not data) will be fully or partially shared with participants through a GitHub repository for educational purposes.
At the end of this session, and by means of a real-world example relevant to the ESTHER project, participants will have a clear idea of the potential of unsupervised ML for RL, as well as the key requirements (both on terms of infrastructure and coding skills) that are needed to successfully develop a RL study.
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