Turning to Machine Learning for Industrial Automation Applications (.PDF Download)

At its core, machine learning studies the construction of algorithms and learns from them to make predictions on data by building models from sample inputs. If we further break it down, machine learning borrows heavily from computational statistics (prediction modeling using computers) and mathematical optimization, which provides methods, theory and application data to those models. In essence, it creates its own data models based on algorithms and then uses them to predict defined patterns within a range of data sets.

Machine-learning algorithms can be broken down into five types: supervised, unsupervised, semi-supervised, active, and reinforcement, all of which act just like they sound. Supervised algorithms are programmed and implemented by humans to provide both input and output as well as furnishing feedback on predictive accuracy during training. Machine learning will then use what it has learned and apply it to introduced data sets. Unsupervised requires no training and relies on “deep learning” (an aspect of AI that automates predictive analytics) to analyze data, which it uses to predict data sets. Semi-supervised is provided with incomplete algorithms or training sets and learns by completing the missing components. Reinforcement learning provides feedback to the program as it completes actions in a dynamic environment and extrapolates predictive data sets by learning from said actions.