Learning normal, unsupervised

ML—the discipline—has many variations and techniques of the process of learning. ML—the feature in the Elastic Stack—uses a specific type, called unsupervised learning. The main attribute of unsupervised learning is that the learning occurs without anything being taught. There is no human assistance to shape the decisions of the learning; it simply does so on its own via inspection of the data it is presented with. This is slightly analogous to the learning of a language via the process of immersion, as opposed to sitting down with books of vocabulary and rules of grammar.

To go from a completely naive state where nothing is known about a situation to one where predictions could be made with good certainty, a model of the situation needs to be constructed. How this model is created is extremely important, as the efficacy of all subsequent actions taken based upon this model will be highly dependent on the model's accuracy. The model will need to be flexible and continuously updated based upon new information, because that is all that it has to go on in this unsupervised paradigm.