Unsupervised Anomaly Detection with Isolation Forest


Follow to receive video recommendations   a   A

This talk will focus on the importance of correctly defining an anomaly when conducting anomaly detection using unsupervised machine learning. It will include a review of Isolation Forest algorithm (Liu et al. 2008), and a demonstration of how this algorithm can be applied to transaction monitoring, specifically to detect money laundering. Slides: https://www.slideshare.net/PyData/unsupervised-anomaly-detection-with-isolation-forest-elena-sharova

Editors Note:

I would like to work with open source projects to create a branch of the tree with all of the best videos for your open source project. Please send me an email if you are interested.