With the rise of sensor data and performance analytics, anomaly detection is a sub-field of data science that has been increasingly popular in many different applications. However, many issues arise when applying traditional machine learning to identify anomalies. In this talk, I will provide an overview of different algorithms for detecting anomalies in clouds of data points, time series, and real-time data streams. At the end of the talk, I will discuss some useful techniques when implementing a real-world system to detect anomalies.
