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Core
Outlier and Anomaly Detection
Outlier and anomaly detection finds points that do not fit the bulk of the data using statistical, distance/density, or reconstruction-based methods. The hard part is that anomalies are rare and usually unlabeled, so the framing is mostly unsupervised, and a robust estimate of normal is what makes the rare point stand out. Applied AI interviews probe it because fraud, fault, and data-quality work all reduce to deciding what counts as normal and at what threshold.
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