Cross-Dataset Time Series Anomaly Detection for Cloud Systems

This course teaches learners how to detect anomalies in cloud systems by utilizing cross-dataset time series anomaly detection techniques. The learning outcomes include understanding the challenges of anomaly detection in cloud systems, implementing the ATAD approach for cross-dataset anomaly detection, and achieving significant performance improvements with minimal labeled data. The course covers skills such as transfer learning, active learning, and building accurate anomaly detection models. The teaching method involves a presentation of research findings and experimental results. This course is intended for individuals interested in cloud computing, anomaly detection, and data analysis in online services.