Bayes Theorem, Old but Gold!
It has more than two centuries but has become the most used Machine Learning algorithms
Bayes Theorem allows anyone, in a deceptively simple manner, to calculate a conditional probability where intuition often fails. You’ve might bump into this theorem in Machine Learning when dealing with Maximum a Posteriori (MAP) — a probability framework for fitting a model to a training dataset — or in classification predictive modeling problems such as the Bayes Optimal Classifier and Naive Bayes.
History of Bayes Theorem
Reverend Thomas Bayes was a wealthy presbyterian minister and amateur mathematician who lived in London in the eighteenth-century. Without realizing it created, the reverend created a completely new religion that influenced a great number of study fields over decades. Europe was living in a very religious controversial era. Scientists were trying to make use of evidence around us to come up with rational conclusions about God.
It is unknown if Bayes wanted to prove the existence of God, but it is historically proven that Bayes wanted to mathematically deal with the issue of cause and effect, which ended in his theorem.
