TABLE 1.

Glossary of computational terms.

Term	Definition
Open-source	“Software that is free to use and can be studied or improved by anyone because it is based on a code that anyone can use” (Cambridge Dictionary, 2023)
Open-access	“Available for everyone to use” (Cambridge Dictionary, 2023)
Algorithm	“A set of rules used to calculate an answer to a mathematical problem” (Cambridge Dictionary, 2023)
Artificial intelligence (AI)	“A research field concerned with understanding and building intelligent entities—machines that can compute how to act effectively and safely in a wide variety of novel situations” (Russell and Norvig, 2003, p. 19)
Machine learning (ML)	“Machine learning is a subfield of AI that studies the ability to improve performance based on experience” (Russell and Norvig, 2003, p. 19)
Deep learning (DL)	“Machine learning using multiple layers of simple, adjustable computing elements (e.g., artificial neurons)” (Russell and Norvig, 2003, p. 44)
Precision	“A performance measure that indicates the ratio of model predictions of a class which actually belong to the predicted class” (Géron, 2019, p. 139)
Graphical user interface (GUI)	“A way of arranging information on a computer screen that is easy to understand and use because it uses icons (pictures), menus, and a mouse rather than only text” (Cambridge Dictionary, 2023)
Source code	“The set of computer instructions that have been written in order to create a program or piece of software” (Cambridge Dictionary, 2023)
Feature extraction	“Obtaining useful information from raw data by applying a series of computations” (Russell and Norvig, 2003, p. 988)
Key-point	“Points of a shape that are prominent according to a particular definition of interestingness or saliency” (Tombari et al., 2013, p. 198)
Clustering	“An unsupervised learning technique which aims to group similar data instances together into clusters” (Géron, 2019, p. 307)
Active learning	“A learning strategy where human experts interact with the learning algorithm, providing labels for specific instances when the algorithm requests them” (Géron, 2019, p. 332)
Supervised learning	“Learning a function that maps from input to output by observing input-output pairs” (Russell and Norvig, 2003, p. 671)
Unsupervised learning	“Learning patterns in the input without any explicit feedback” (Russell and Norvig, 2003, p. 671)
Dimensionality reduction	“Reducing the number of features in the data in order to improve the performance of machine learning models, or to visualize the data” (Géron, 2019, p. 279)
Neuronal networks	“A very simplified model of our neuronal circuitry, composed of a stack of layers of artificial neurons” (Géron, 2019, p. 5)
Accuracy	“A performance measure that indicates the ratio of correctly classified data instances.” (Géron, 2019, p. 22)
Heuristics	“Experience-based techniques for problem-solving, learning, and discovery. Heuristic solutions are not guaranteed to be optimal, but heuristic methods are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical” (Martí et al., 2018, p. V)
Modular programming	“A design technique that divides a complex system into several parts, where each part performs a single function, and can be developed or tested independently” (Yau and Tsai, 1986, p. 714)