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 |