| Title | Unsupervised Learning | |
| Duration | 60 mins | |
| Module | A | |
| Lesson Type | Lecture | |
| Focus | Practical - AI Modelling | |
| Topic | Data analysis |
Unsupervised learning,Clustering,Advanced Clustering,Ethics,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
You can base this class around the slides.
| Duration (min) | Description | Concepts |
|---|---|---|
| 5 | Unsupervised learning | Intro to unsupervised learning (vs supervised) |
| 20 | Clustering | KMeans, K-Medoid, optimal K |
| 20 | Advanced Clustering | Hierarchical clustering, DBSCAN |
| 5 | Advanced Dimensionality reduction | advanced dimensionality reduction: t-SNE |
| 5 | Ethics | Connection to Ethics Case study |
Clustering people may enable predictions that violate privacy or draw unjust inferece, like in the following cases
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068. The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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