Understanding Anomaly Detection

Anomaly detection plays a key role in today’s world of data-driven decision making. This stems from the outsized role anomalies can play in potentially skewing the analysis of data and the subsequent decision making process. This course is an overview of anomaly detection’s history, applications, and state-of-the-art techniques.

Taught by anomaly detection expert Arun Kejariwal, the course provides those new to anomaly detection with the understanding necessary to choose the anomaly detection techniques most suited to their own application. While not required, a basic understanding of statistics, R, and Python will be helpful to get the most out of the class.

Survey the history of anomaly detection in astronomy, statistics, and manufacturing
Gain a core understanding of the most important anomaly detection techniques available today
Explore the landscape of applications where anomaly detection is routinely used
Develop an awareness of the underlying assumptions and challenges of anomaly detection
Learn how to mitigate the influence of anomalies during data-driven decision making processes

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