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 Big Data and Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems including stock data analysis, fraud detection, credi risk etc.. We will also demonstrate, using R, Python, Apache Spark, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn
In this workshop, you will learn the core techniques used in Anomaly detection. Through examples in R, Python and Apache Spark,Keras and Tensorflow you will learn how to methodically apply various anomaly techniques. In addition, you will learn
Data science techniques using supervised and unsupervised machine learning techniques are becoming increasing popular in identifying patterns in data and to build systems that can better predict the future. With increase in volume, variety and velocity of data, many big data techniques are being used to scale these techniques today.
However, Anomaly detection techniques focus on detecting the asymetric outliers in the data sets. Many techniques are used for anomaly detection and a comprehensive understanding of these techniques would help get a better understanding of the nature of the data and to detect and act upon these outliers.
In module one, we will review the core techniques in anomaly detection. Through examples we will understand the different outlier detection techniques and review evaluation criteria
In module two, we will discuss advanced techniques in anomaly detection and use Apache Spark for anomaly detection. We will also discuss best practices in scaling and using anomaly detection techniques.
SAMPLE FREE LECTURE
Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..