Problem Motivation

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