Machine Learning Systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine.

Human intuition cannot be entirely eliminated since the designer of the system must specify how the data are to be represented and what mechanisms will be used to search for a characterization of the data.

Machine learning can be viewed as an attempt to automate parts of the scientific method. Some machine learning researchers create methods within the framework of Bayesian statistics.

Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true.

The name "Bayesian" comes from the frequent use of Bayes' theorem in the inference process. Bayes' theorem was first derived by the Reverend Thomas Bayes.

Bayesian inference uses aspects of the scientific method, which involves collecting evidence that is meant to be consistent or inconsistent with a given hypothesis.

As evidence accumulates, the degree of belief in a hypothesis changes.

With enough evidence, it will often become very high or very low. Thus, it could in theory be considered a suitable logical basis to discriminate between conflicting hypotheses- hypotheses with a very high degree of belief should be accepted as true those with a very low degree of belief should be rejected as false.

In practice, while the general mathematical framework of Bayesian inference does hold, it necessitates the assigning of a priori probabilities to hypotheses that might be subject to arbitrary bias.