> For the complete documentation index, see [llms.txt](https://ersilia.gitbook.io/esther-workshop/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ersilia.gitbook.io/esther-workshop/materials/ml3-advanced-course-s1.md).

# ML3 - Advanced course: S1

## Record linkage with unsupervised ML

### Content

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.

### Learning outcomes

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.

### Moodle

* See [Moodle](https://swisstph.ecampus.ssphplus.ch/mod/folder/view.php?id=9874) for more!