Problem Motivation

Problem common sighted in unsupervised learning as well as supervised learning

Suppose we have set of features that each of them is going to test the aircraft engine.
In the example above we just have two set of features that test the engine, so far engines worked well based on the given red mark
Then we add new engine and make two feature to that engine. It turns out that the test reveal the engine being far away from the rest of the engine.
This is what known in Machine Learning as Anomaly Detection.

p(x) is the probability of features
sigma in here denotes some threshold value

Here we know checking how engine that we tested is less or more than particular threshold value(discussed later, computed by the Gaussian Distribution)

fraud detection user example
x's = denotes how much user login, typing speed, activity, dll
technique used by web to analyzed human behaviour

Manufacturing like jet plane example earlier

AD on monitoring computers. if one of features is behaving strangely, then there's must be something wrong for the computer

SUMMARY

Next Gaussian distribution and gaussian algorithm for the tools that used for checking anomaly detection