TransferLab Training: Practical Anomaly Detection - Module 6: Extreme Value Theory for AD скачать в хорошем качестве

TransferLab Training: Practical Anomaly Detection - Module 6: Extreme Value Theory for AD

In this module, we introduce extreme value theory (EVT) as a method for finding thresholds for anomaly detection. EVT is based on the assumption that the scores of anomalous points are significantly higher than the scores of nominal points. It estimates the tail of the score distribution and uses this to find a probabilistically interpretable threshold. We apply EVT to the New York taxi dataset to find a threshold for detecting anomalies in the number of taxi rides. Accessing the resources of this course: 🔗 GitHub Repository: complete code and examples on our GitHub https://github.com/aai-institute/tfl-... 🌐 Website: digests of the latest research on the TransferLab’s website: https://transferlab.ai/ 🎓 Full Course & Certification (For FREE): Enroll in our full video course available on our learning platform. Complete the course at your own pace and earn a certificate for free to enhance your portfolio: https://lms.appliedai-institute.de/ 00:50 Extreme Value Theory (EVT) 09:30 Exercise: Playing with the GEV Parameters, Loading Data and Setting up Block Maxima 11:30 Solution: Playing with the GEV Parameters, Loading Data and Setting up Block Maxima 17:48 Exercise: MLE for the GEV 18:12 Solution: MLE for the GEV 26:26 Exercise: Improving the MLE fit 27:25 Q-Q Plot 28:52 Solution: Improving the MLE fit 33:51 Exercise: Uncertainty Estimation in GEV Distributions 35:39 Solution: Uncertainty Estimation in GEV Distributions 37:27 Exercise: GEV Distributions for Minima 38:19 Solution: GEV Distributions for Minima 40:01 Example: Comparison with the Z-test 41:45 Taking a Look Back at the Theory 50:40 Proofing the Fisher-Tipett-Gnedenko Theorem 52:21 Exercise: The Effect of the Block Size on the GEV Distribution 53:14 Solution: The Effect of the Block Size on the GEV Distribution 55:12 The Peaks Over Thresholds Method for Anomaly Detection 01:00:22 Exercise: Using the GPD for Anomaly Detection 01:02:03 Solution: Using the GPD for Anomaly Detection 01:08:55 More about Anomaly Detection 01:12:50 Optional Exercise: Anomaly Detection with EVT 01:13:49 Things That Were Omitted The appliedAI Institute for Europe gGmbH is supported by the KI-Stiftung Heilbronn gGmbH.