Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.

In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.

Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.

What you will learn:

• Anomaly Detection: An introduction

• Graphical and Exploratory analysis techniques

• Statistical techniques in Anomaly Detection

• Machine learning methods for Outlier analysis

• Evaluating performance in Anomaly detection techniques

• Detecting anomalies in time series data

• Case study 1: Anomalies in Freddie Mac mortgage data

• Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow

See sample here:  https://www.slideshare.net/QuantUniversity/anomaly-detection-core-techniques-and-advances-in-big-data-and-deep-learning