![](https://lms.appliedai-institute.de/pluginfile.php/593/course/overviewfiles/Frame%202056%20%281%29.png)
Detecting anomalies is of high interest in multiple industries for identifying safety and security risks, ensuring production quality, or finding new business opportunities. However, anomaly detection faces some unique challenges. First, identifying anomalies by hand is difficult, especially in multidimensional data. Second, anomalies are usually poorly represented in datasets.
Anomaly detection must therefore rely largely on unsupervised learning with possibly contaminated nominal data. These methods need additional assumptions about the data to be able to identify anomalies reliably. In this online course, we will review common approaches for anomaly detection and discuss their strengths and weaknesses in different application areas.
Upon completion of the course, participants will be able to evaluate and compare the performance of diverse anomaly detection algorithms, refining their ability to discern the most effective methods for specific application areas. This comprehensive review of common approaches, empowers practitioners to make informed decisions, enhancing their proficiency in anomaly detection for various use cases.