Introduction to Anomaly Detection in R

Description

In this course, you will:

• Learn how to utilize numerical and graphical summaries to analyze whether data contains outliers. 
• You'll utilize Grubbs' test to determine whether a point is an outlier, and you'll learn about the Seasonal-Hybrid ESD technique, which can help find outliers when the data is a time series.
• Learn how to compute the k-nearest neighbors distance and the local outlier factor, which are used to generate continuous anomaly scores for each data point when there are numerous features in the data. 
• Discover the distinction between local and global abnormalities and how the two methods can assist in each scenario.
• Investigate an isolation forest, a rapid and robust way of finding anomalies that analyzes how readily points can be isolated by randomly splitting the data into smaller and smaller sections.
• Learn to compare the detection performance of the algorithms when tagged anomalies are present. 
• Learn how to compute and evaluate the precision and recall statistics for an anomaly score, as well as how to modify algorithms to accommodate data containing categorical features.

Syllabus:

• Statistical outlier detection
• Distance and density based anomaly detection
• Isolation forest
• Comparing performance