I grew up being a Bayesian, apparently

Little did I know when I started my career as a research physicist at CERN that I was a member of the Bayesianist “tribe”. In fact, I was not even aware back then that what we called data analysis “another day working with data” was even a branch of the Machine Learning religion.

The content below is from the The Master Algorithm by Pedro Domingos. Formatting all mine.

Tribe Premise Master Algorithm
Symbolists All intelligence can be reduced to manipulating symbols Inverse Deduction: It figures out what knowledge is missing in order to make a deduction go through, and then makes it as general as possible
Connectionists Learning is what the brain does, and we need to reverse engineer it Back Propagation: It compares a system’s output with the desired one and then successively changes the connections in layer after layer of neurons so as to bring the output closer to what it should be
Evolutionaries The mother of all learning is natural selection Genetic programming: It mates and evolves computer programs in the same way that nature mates and evolves organisms
Bayesians All learned knowledge is uncertain, and learning itself is a form of uncertain inference Bayes’ theorem: It tells us how to incorporate new evidence into our beliefs, and probabilistic inference algorithms do that as efficiently as possible
Analogizers The key to learning is recognizing similarities between situations and thereby inferring other similarities. Support vector machine: It figures out which experiences to remember and how to combine them to make new predictions