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AI & Machine Learning · Intermediate

Anomaly Detection

Identifying data points, events or patterns that deviate significantly from expected behavior.

Definition

Anomaly detection can be unsupervised (isolation forest, DBSCAN, autoencoders), semi-supervised (trained on normal data only) or supervised when labeled anomalies exist. It is used for fraud detection, equipment monitoring and security analytics. Because anomalies are rare, evaluation focuses on precision and recall rather than accuracy.

Example

Flagging a sudden spike in login attempts from a new country.

Exam tip

Random Cut Forest (AWS) and Isolation Forest are common named algorithms for unsupervised anomaly detection.

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