Machine Learning refers to the general field of study that deals with automated statistical learning and pattern detection by non-biological systems. It can be seen as a classical sub-domain of artifical intelligence that specifically deals with data analysis, modeling and prediction through the knowledge extracted from the previous (training) samples. As a multi-disciplinary area, it has borrowed concepts and ideas ranging from pure mathematics to cognitive science, all the while trying to exhaustively describe learning systems.
When considering the most used algorithms in Machine Learning, we can take on multiple approaches to describe its subdivisions. It's possible to create a simple taxonomy based on their most prominent characteristics and implementations, such as the suggested by Lotte and colleagues:
Firstly, the most widely distinction is usually made between the generative or unsupervised (k-NN, k-means clustering, …) vs discriminative or supervised (Support Vector Machines, Linear Discriminant Analysis, …) ones – while the first is able to spontaneously generate different categories based purely on the data structure, the second kind is only able of distinguishing previously learned classes (through the feeding of correctly identified data). This is probably the most prominent distinction between Machine Learning methods....