Computer systems patterned after human intelligence in their ability to learn and recognize so that previously unseen information can be acted on in ways the produce useful results. The foundations of AI include logic, mathematics, probability, logic, decision theory, neuroscience, and linguistics.
Algorithms loosely modeled after the human brain, with layers of connected elements that send information to each other in the way human neurons interact.
Data sets that are so large or complex that traditional data processing applications are inadequate to deal with them.
Algorithms with output and decision-making processes that cannot readily be explained by developers or the computer itself.
Classification algorithms enable machines to assign a category to a data point based on training data.
Clustering algorithms let machines group data points or items into groups with similar characteristics.
Coefficients indicate the relationship of independent variables to the dependent variable in a model. Positive coefficients show that as the independent variable moves upwards, so does the dependent variable. Negative coefficients indicate that as the coefficient goes down, so does the dependent variable.
Use of AI to examine and interpret images to define or recognize them like the way humans see.
An interdisciplinary field encompassing scientific processes and systems that extract knowledge or insights from data in various forms, either structured or unstructured. It is an extension of data analysis fields such as statistics, machine learning, data mining, and predictive analytics.
A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart.