Anomaly detection in machine learning involves identifying unusual data points that don't conform to expected patterns, such as in bank transaction alerts.
It plays a critical role in various scenarios like monitoring system performance, fraud detection, and predictive maintenance.
Approaches include supervised learning (using labeled data), unsupervised learning (detecting anomalies in mostly normal data), and semi-supervised learning (training on normal data).
Popular ML algorithms for anomaly detection include Isolation Forest, One-Class SVM, Autoencoders, and LSTM for time series data.