Anomaly detection (or outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such as e.g. bank fraud, medical problems, structural defects etc. This connection makes it more challenging to decide which data points can be considered anomalies especially if no labels are provided (Unsupervised Learning). Possibly, some normal or anomalous examples can be labeled, in addition to a large set of unlabeled data (Semi-supervised Learning). This seminar focuses on discussing state-of-the-art research publications dedicated for Unsupervised and Semi-supervised Anomaly detection.