With the proliferation of massive amounts of unlabeled data, unsupervised learning
algorithms–which can automatically discover interesting and useful patterns in such data …

Many industry experts consider unsupervised learning the next frontier in artificial
intelligence, one that may hold the key to general artificial intelligence. Since the majority of …

Design clever algorithms that discover hidden patterns and draw responses from
unstructured, unlabeled data. Key FeaturesBuild state-of-the-art algorithms that can solve …

Anomaly detection is the process of identifying unexpected items or events in datasets,
which differ from the norm. In contrast to standard classification tasks, anomaly detection is …

Supervised and unsupervised learning algorithms have shown a great potential in
knowledge acquisition from large data sets. Supervised learning reflects the ability of an …

One way to describe anomalies is by saying that anomalies are not concentrated. This leads
to the problem of finding level sets for the data generating density. We interpret this learning …

Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and
unsupervised learning problems. In this paper we are concerned with investigating the …

Support Vector Machines (SVMs) have been one of the most successful machine learning
techniques for the past decade. For anomaly detection, also a semi-supervised variant, the …

Anomaly detection involves identifying rare data instances (anomalies) that come from a
different class or distribution than the majority (which are simply called “normal” instances) …

In this tutorial we aim to present a comprehensive survey of the advances in deep learning
techniques specifically designed for anomaly detection (deep anomaly detection for short) …