Administrative Information
| Title | Unsupervised Learning |
| Duration | 60 mins |
| Module | A |
| Lesson Type | Lecture |
| Focus | Practical - AI Modelling |
| Topic | Data analysis |
Keywords
Unsupervised learning,Clustering,Advanced Clustering,Ethics,
Learning Goals
- learn basics of unsupervised learning
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Python
- pandas
Optional for Students
None.
References and background for students
None.
Recommended for Teachers
None.
Lesson materials
Instructions for Teachers
You can base this class around the slides.
Outline/time schedule
| 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 |
Connection to Ethics Case study
Clustering people may enable predictions that violate privacy or draw unjust inferece, like in the following cases
- Target Figured Out A Teen Girl Was Pregnant Before Her Father Did
- The childcare benefits affair (toeslagenaffaire)
- Websites Vary Prices, Deals Based on Users' Information (see At Examples for teaching ethical AI)
Acknowledgements
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.