Machine learning is on its way to become one of the foundational disciplines for the next decade of software development. In both the enterprise and consumer markets, machine learning is helping reimagine how software application interpret and process data. However, more often than not, people refer to machine learning as a big discipline when in reality is an overarching theme that groups different schools of thoughts.
In his recent book The Master Algorithm, computer science researcher Pedro Domingos uses a taxonomy based on five major schools of thoughts in machine learning:
· The Symbolists: This group of machine learning practitioners focus on the premise of inverse deduction. Instead of the classical model of starting with a premise and looking for the conclusions, inverse deduction starts with a set of premises and conclusions and works backward to fill in the gaps.
· The Connectionists: This subset of machine learning is one of the most well-known as their focus on re-engineering the brain. The most famous example of the connectionist approach is what today we call “Deep Learning”. At a high level, this approach is based on connecting artificial neurons in a neural network. Connectionist techniques are very efficient in areas such as image recognition or machine translation.
· The Evolutionaries: This machine learning discipline focuses on applying the idea of genomes and DNA in the evolutionary process to data processing. In essence, evolutionary algorithms will constantly evolve and adapt to unknown conditions and processes.
· The Bayesians: Another well-known group within machine learning, the Bayesians focus on handling uncertainty using techniques like probabilistic inference. Vision learning and spam filtering are some of the classic problems tackled by the Bayesian approach. Typically, Bayesian models will take a hypothesis and apply a type of “a priori” thinking, believing that there will be some outcomes that are more likely. They then update a hypothesis as they see more data.
· The Analogizers: This machine learning discipline focuses on techniques to match bits of data to each other. The most famous analogizer model is the “nearest neighbor” algorithm which can give results to neural network models.
Machine Learning Technologies Combine the Five Tribes
Even though each tribe represents a different school of thought within the machine learning space, they are often combined to attack problems from different angles. As a result, most of the modern machine learning platform leverage algorithms from all five school of thoughts often combining them to enable robust machine learning capabilities.