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Introduction to Anomaly Detection

This module introduces the concept of an anomaly, or outlier, and different techniques for identifying these unusual data points. First, the general concept of an anomaly is discussed and demonstrated in the business community via the detection of fraud, which in general should be an anomaly when compared to normal customers or operations. Next, statistical techniques for identifying outliers are introduced, which often involve simple descriptive statistics that can highlight data that are sufficiently far from the norm for a given data set. Finally, machine learning techniques are reviewed that can either classify outliers or identify points in low density (or outside normal clusters) areas as potential outliers.

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