This repository includes the laboratory work I did during the Anomaly Detection course, which I took in my first year of an MSc in Artificial Intelligence at the Faculty of Mathematics and Computer Science, University of Bucharest.
- L1: introduction, blobs generation, ROC-AUC curves, and other metrics
- L2: leverage score, k-nearest Neighbours (kNN), local outlier factor (LOF)
- L3: isolation forest-based methods (IF, EIF, DIF), lightweight on-line detector of anomalies (LODA)
- L4: one-class support vector machine (OC-SVM)
- L5: dimensionality reduction, principal component analysis (PCA), autoencoders (AE)
- L6: graphs based anomalies detection