Navigation: <-- Part VI: Principles That Transfer (Reflection) | Main Index | Part VIII: 3rd Pass — Deep Learning -->

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Part VII: 2nd Pass - Unsupervised Learning

Supervised learning requires labeled data. This part introduces the complementary setting: no labels, no correct answers. You will see the two main problem types, grouping and detection, and work through the evaluation challenge that defines this domain. The CRISP-DM inner loop applies exactly as before, but with different tools for measuring progress.

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Nuggets in This Part

#	Nugget	Prerequisites
1	Framing Unsupervised Learning	Supervised Learning · CRISP-DM
2	k-Means Clustering	Framing Unsupervised Learning · Hyperparameter Optimization
3	Anomaly Detection	Framing Unsupervised Learning
4	Isolation Forests	Anomaly Detection · Random Forests

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Script v1.4 (2026-06-10) · FGN