ABSTRACT
Machine Learning (ML) methods allow the reader to make predictions and draw conclusions from data. There are, of course, several ML methods that are much more systematic and accurate than the rough estimate the people have just determined between the “solid” and “vapor” classes to determine thresholds and achieve classification tasks. In unsupervised learning, the ML algorithm allows learning patterns in the input without explicit feedback. Clustering is arguably the most common unsupervised learning method. The Support Vector Machine algorithm constructs what is known as the maximum margin separator. An understanding of how rationality and intelligence emerge from the brain has remained elusive so far. The human brain's neurons were the inspiration for the development of mathematical models for neurons, giving rise to the perceptron and, eventually, to the multi‐layer neural network that is now used to learn complex mappings between inputs and outputs.
