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Unsupervised Anomaly Detection with Isolation Forest

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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

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