Carnegie Mellon PhD student Yue Zhao collects this great Github repository of anomaly detection resources: https://github.com/yzhao062/anomaly-detection-resources

The repository consists of tools for multiple languages (R, Python, Matlab, Java) and resources in the form of:

1. Books & Academic Papers
2. Online Courses and Videos
3. Outlier Datasets
4. Algorithms and Applications
5. Open-source and Commercial Libraries/Toolkits
6. Key Conferences & Journals

Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.

https://github.com/yzhao062/anomaly-detection-resources

Quick Access — Table of Contents

• 1. Books & Tutorials
  • 1.1. Books
  • 1.2. Tutorials
• 2. Courses/Seminars/Videos
• 3. Toolbox & Datasets
  • 3.1. Multivariate data outlier detection
  • 3.2. Time series outlier detection
  • 3.3. Real-time Elasticsearch
  • 3.4. Datasets
• 4. Papers
  • 4.1. Overview & Survey Papers
  • 4.2. Key Algorithms
  • 4.3. Graph & Network Outlier Detection
  • 4.4. Time Series Outlier Detection
  • 4.5. Feature Selection in Outlier Detection
  • 4.6. High-dimensional & Subspace Outliers
  • 4.7. Outlier Ensembles
  • 4.8. Outlier Detection in Evolving Data
  • 4.9. Representation Learning in Outlier Detection
  • 4.10. Interpretability
  • 4.11. Outlier Detection with Neural Networks
  • 4.12. Active Anomaly Detection
  • 4.13. Interactive Outlier Detection
  • 4.14. Outlier Detection in Other fields
  • 4.15. Outlier Detection Applications
  • 4.16. Emerging and Interesting Topics
• 5. Key Conferences/Workshops/Journals
  • 5.1. Conferences & Workshops
  • 5.2. Journals