TransferLab Training: Practical Anomaly Detection
This workshop introduces unsupervised ML techniques for anomaly detection, their strengths and weaknesses and different application areas.
Content
Identification of anomalies is of high interest in multiple industries for identifying safety and security risks, ensuring production quality, or finding new business opportunities. But anomalies are hard to detect because they are characteristically poorly represented in datasets.
Part 1: introduction, algorithms, exercises
- Informal notion of anomaly and the types of anomaly detection like unsupervised learning, one class problem, class imbalances, etc.
- Concise introductions to several algorithms, each chosen to represent a certain approach to anomaly detection.
- Contamination Framework. Assumptions of the different algorithms
- Anomaly Detection via Density Estimation.
- Anomaly Detection via Isolation.
- Awareness for relevant problem parameters like the degree of contamination, the clusteredness of anomalies, irrelevant dimensions, etc.
- Evaluate and compare the algorithms' performance.
- Anomaly Detection via reconstruction error.
Part 2: anomaly detection in time series
- Anomaly types: Point, context and pattern anomalies.
- Preprocessing techniques for anomaly detection in time series.
- Context anomalies, regimes and the hidden Markov model.
- Pattern anomalies and maximal discords.
- Extreme Value Theory and GEV distributions.
- Exercises. Detecting low and high values. Exploring, studying and detecting anomalies ride-share data.
Disclaimer
The appliedAI Institute for Europe gGmbH is supported by the KI-Stiftung Heilbronn gGmbH.
The appliedAI Institute for Europe gGmbH is a subsidiary of the appliedAI Initiative GmbH.
