This free A-Z AI glossary is a comprehensive set of terms related to Artificial Intelligence (AI) and machine learning, organized alphabetically from “A” to “Z”. Each entry includes a brief definition or description of the term, covering a wide range of concepts, algorithms, techniques, and technologies within the AI field.

Page last updated 8 December 2023
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Activation Functions
‍Functions in a neural network that help to determine the output of a neural network. Common examples include sigmoid, tanh, and ReLU (Rectified Linear Unit).

Active Learning
‍A special case of machine learning where a learning algorithm is able to interactively query the user or some other information source to obtain new data points to be added to the training set.

Adversarial Examples
‍Inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; typically used to test or manipulate algorithms.

Adversarial Machine Learning
‍A technique in machine learning where a model is trained to identify and counter adversarial examples, which are inputs designed to confuse the model.

Algorithm
‍A set of rules or instructions given to an AI, computer program, or other machine to help it perform a specific task or solve problems.

Algorithmic Bias
‍Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.

Algorithmic Efficiency
Refers to the computational complexity and resource usage of an algorithm, important in AI for optimizing the performance and scalability of models.

Anomaly Detection
A technique used in AI to identify unusual patterns that do not conform to expected behavior. It is widely used in fraud detection, system health monitoring, and outlier detection in data analysis.

Artificial Intelligence (AI)
A field of computer science dedicated to creating systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Attention Mechanisms
In neural networks, especially in the field of natural language processing, attention mechanisms help the model focus on specific parts of the input for making decisions, similar to how humans pay attention to certain aspects of visual or auditory inputs.

Augmented Analytics
The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.

Autoencoder
A type of artificial neural network used to learn efficient codings of unlabeled data, typically for dimensionality reduction.

Autoencoders
Neural networks used to recreate inputs to their outputs while learning a representation of the data.​Sure, here are the terms beginning with "B" along with their meanings:

Adversarial Training
A technique in machine learning where a model is trained with adversarially perturbed inputs (e.g., modified images) to improve its robustness against adversarial attacks.

Attention Networks
Neural network architectures that focus on different parts of the input sequentially, rather than simultaneously, improving the model's performance in tasks like image and language understanding.

AutoML (Automated Machine Learning)
The process of automating the end-to-end process of applying machine learning to real-world problems.

Active Learning
A type of semi-supervised machine learning where the algorithm chooses the data it learns from.

Attentional Biases
Mechanisms in deep learning models, particularly in NLP, that help to focus on specific parts of the input sequence, improving the context understandin.
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Backpropagation
A method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data is processed. It is a key part of training deep learning models.

Backpropagation Through Time (BPTT)
An algorithm used to train certain types of neural networks, with an application particularly to recurrent neural networks.

Bagging (Bootstrap Aggregating)
A machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.

Batch Normalization:
A technique for improving the speed, performance, and stability of artificial neural networks. It normalizes the input layer by adjusting and scaling the activations.

Bayesian Network
A probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).

Bayesian Networks
A type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.

Bayesian Optimization
A method of optimizing objective functions that are expensive to evaluate. It is particularly suited for optimization of high-cost functions, situations where the balance between exploration and exploitation is important.

Bias Node
In neural networks, a bias node is an additional input to each pre-activation function in the network layers, used to shift the activation function to either the left or the right.

Bias-Variance Tradeoff
A fundamental problem in supervised learning where increasing the bias will decrease the variance and vice versa. It's the tradeoff between the model's ability to minimize bias and variance.

Biometrics
The measurement and statistical analysis of people's unique physical and behavioral characteristics, primarily for authentication and access control.

Boltzmann Machine
A type of stochastic recurrent neural network that can learn a probability distribution over its set of inputs.

Boosting
A machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.

BERT (Bidirectional Encoder Representations from Transformers)
A transformer-based machine learning technique for natural language processing pre-training
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Capsule Network
A deep learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and differentiate one from the other.

Capsule Networks
A type of artificial neural network designed to improve the efficiency and accuracy of learning in AI systems. Capsule networks aim to overcome some of the limitations of convolutional networks by preserving the hierarchical spatial relationships between parts of an object.

Catastrophic Forgetting
A phenomenon in machine learning where a model forgets previously learned information upon learning new information, most common in neural networks.

Chatbot
A software application used to conduct an online chat conversation via text or text-to-speech, instead of providing direct contact with a live human agent. Designed to convincingly simulate the way a human would behave as a conversational partner.

Cognitive Architecture
A hypothesis about the structure and content of human cognition, often used as a guide for building AI systems.

Cognitive Computing
A technology platform that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition.

Collaborative Filtering
A method used by recommender systems to make predictions about the interests of a user by collecting preferences from many users.

Combinatorial Optimization
A field of optimization whose objective is to find the best solution from a finite set of solutions.

Computational Complexity
In computational theory, it refers to the amount of resources required for the execution of algorithms, such as time and storage.

Computer Vision
A field of AI that trains computers to interpret and understand the visual world. Machines can accurately identify and locate objects then react to what they "see" using digital images from cameras, videos, and deep learning models.

Convex Optimization
A subfield of optimization that studies the problem of minimizing convex functions over convex sets. Its applications are widespread in machine learning algorithms.

Convolutional Neural Network (CNN)
A deep learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and differentiate one from the other.​Certainly! Here are the terms beginning with "D" along with their meanings:

Causal Inference
The process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

CapsNet (Capsule Networks)
An advanced neural network structure that aims to improve the efficiency of neural networks, particularly in tasks involving image recognition.

Cross-Validation
A technique in machine learning to evaluate the predictive performance of a model by partitioning the original sample into a training set to train the model, and a test set to evaluate it.

Data Augmentation
The process of increasing the size and diversity of a data set for training machine learning models by making modifications to the data, such as rotations or translations of images.

Data Labeling
The process of identifying raw data (like images, text files, videos) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it.

Data Mining
The process of discovering patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the web, etc.

Data Wrangling
The process of cleaning, structuring, and enriching raw data into a desired format for better decision making in less time.

Decision Boundary
In machine learning, it's a surface that separates data points belonging to different class memberships.

Decision Trees
A decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.* **Deep Belief Network:** A generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables with connections between the layers but not between units within each layer.

Deep Learnin
A subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Deep Reinforcement Learning
Combining deep learning with reinforcement learning, where the artificial neural networks have many layers (deep architectures) to process the state of the environment.

Deepfake
A technique for human image synthesis based on artificial intelligence. It is used to combine and superimpose existing images and videos onto source images or videos using a machine learning technique known as generative adversarial network.

Dimensionality Reduction
The process of reducing the number of random variables under consideration by obtaining a set of principal variables.

Directed Acyclic Graph (DAG)
A finite directed graph with no directed cycles, often used in representing structures in machine learning algorithms.

Dropout
A regularization technique in neural networks where randomly selected neurons are ignored during training, which helps in preventing overfitting.​Certainly! Here are the terms beginning with "E" along with their meanings:

Data Fusion
The process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.

Distributed Computing
A field of computer science that studies distributed systems, where many computers are connected via a network to solve a complex problem.

Data Standardization
A data preprocessing technique that involves rescaling the features so that they have a mean of 0 and a standard deviation of 1.

Deep Q-Network (DQN)
Combines Q-Learning with deep neural networks, for solving reinforcement learning problems
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Eigenvector
In mathematics, an eigenvector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it.
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Embedded Systems
Computer systems with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints, used in a variety of applications including AI.

Embedding Layer
In neural networks, especially deep learning, an embedding layer maps discrete categorical variables to a continuous vector space, often used in processing text in natural language processing.

Embeddings
In the context of machine learning, particularly natural language processing, embeddings are a type of word representation that allows words with similar meaning to have a similar representation in a vector space.

Ensemble Learning
A machine learning paradigm where multiple models (often called "weak learners") are trained to solve the same problem and combined to get better results.

Ensemble Methods
Techniques that combine several base models to produce one optimal predictive model in machine learning, such as random forests and gradient boosting.

Ensemble Techniques
Methods in machine learning that combine multiple models to improve predictions, such as bagging, boosting, and stacking.

Evolutionary Algorithm
Algorithms that use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection

Evolutionary Computation
A subfield of artificial intelligence involving combinatorial optimization problems, which are solved using techniques inspired by biological evolution, such as reproduction, mutation, recombination, and selection.

Explainability
In AI, it refers to a model's ability to explain its reasoning and decision-making process in a way that is understandable to humans.

Expert Systems
A type of artificial intelligence system that uses a knowledge base of human expertise for problem-solving, capable of mimicking the decision-making ability of a human expert.​Here are the AI terms beginning with "F" along with their meanings:

Echo State Networks
A type of recurrent neural network with a sparsely connected hidden layer, used in time series prediction and other tasks.

Edge Computing
A distributed computing paradigm that brings computation and data storage closer to the sources of data, improving response times and saving bandwidth.

Embedding Space
The vector space to which categorical data is transformed in processes like word embeddings.
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False Positive Rate
In statistical classification, it is the proportion of negatives cases that were incorrectly classified as positive, commonly used in the context of binary classification.

Feature Engineering
The process of using domain knowledge to create features that make machine learning algorithms work better.

Feature Extraction
The process of reducing the number of resources required to describe a large set of data accurately. When performing analysis of complex data, one of the major problems stems from the number of variables involved.

Feature Importance
A technique used to determine the significance of different input variables for a predictive model in machine learning.

Feature Selection
The process of selecting a subset of relevant features (variables, predictors) for use in model construction.

Feature Vector
An n-dimensional vector of numerical features that represent some object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics of an object.

Federated Learning
A machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients.

False Positive Rate
A measure in classification models that indicates the proportion of negative examples that were incorrectly classified as positive.

Fuzzy Control System
A control system based on fuzzy logic, which is a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 or 1.

Fuzzy Logic
A form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. It is used to handle the concept of partial truth, where the truth value may range between completely true and completely false.

Fuzzy Set Theory
A form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, used to handle the concept of partial truth.​
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Feature Scaling
The process of normalizing the range of independent variables or features of data
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GAN (Generative Adversarial Network)
A class of machine learning frameworks designed by opposing networks, one generating candidates and the other evaluating them.

Gated Recurrent Unit (GRU)
A type of recurrent neural network (RNN) used in deep learning that can remember values over arbitrary time intervals.

Gaussian Process
A probabilistic model under which observations occur in a continuous domain, e.g., time or space.

Genetic Algorithm
A heuristic search and optimization technique inspired by the principles of genetics and natural selection. It is used to solve complex optimization and search problems.

Genetic Algorithm
Search algorithms based on the mechanics of natural selection and genetics, used in AI for solving optimization and search problems

Genetic Programming
An evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.

Generative Adversarial Network (GAN)
A class of machine learning frameworks designed by opposing networks, one generating candidates and the other evaluating them.

Generative Models
A class of statistical models that generate new data instances, potentially after learning the characteristics of a set of input data. Examples include Generative Adversarial Networks (GANs) and Variational Autoencoders.

Gradient Boosting Machines (GBM)
A machine learning technique for regression and classification problems, producing a prediction model in the form of an ensemble of weak prediction models, typically decision trees.

Gradient Clipping
A technique used in training neural networks, involving modifying the gradients to prevent very large updates to weights during training.
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GANs (Generative Adversarial Networks)
Frameworks for training generative models, typically involving a system of two neural networks contesting with each other in a zero-sum game framework.

Graph Neural Networks (GNNs)
Neural networks that directly operate on the graph structure, allowing for modeling of relationships and interactions in data.
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Graph Convolutional Networks (GCNs)
Neural networks that work directly on graphs and leverage their structural information.
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Hebbian Learning
A theory in neuroscience that proposes an explanation for the adaptation of neurons in the brain during the learning process, described as "cells that fire together, wire together.

Heuristic Function
In algorithms, particularly in problem-solving and AI, a heuristic function is a function that ranks alternatives at each branching step in search algorithms based on available information to decide which branch to follow.

Heuristic Search
A technique used for problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for reaching an immediate goal.

Hierarchical Clustering
A method of cluster analysis which seeks to build a hierarchy of clusters, either by merging smaller clusters into larger ones or by splitting larger clusters.

Hyperbolic Tangent (tanh)
A mathematical function that is often used as an activation function in neural networks.

Hyperparameters
Parameters whose values are set before the learning process begins in machine learning algorithms, as opposed to the values of other parameters which are derived via training.

Hyperplane
In machine learning, particularly in the context of SVMs (Support Vector Machines), a hyperplane is a decision plane which separates between a set of objects having different class memberships.​
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Homomorphic Encryption
A form of encryption allowing one to perform calculations on encrypted data without decrypting it, useful in privacy-preserving data analysis.

Heterogeneous Data
Data that comprises different formats, types, or sources, often encountered in big data analytics.
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Image Recognition
The ability of AI to detect and identify objects or features in a digital image or video.

Imbalanced Data
A problem in machine learning where the classes are not represented equally in the dataset, which can lead to biased or inaccurate models.

Imputation
In statistics and machine learning, the process of replacing missing data with substituted values.

Inception Network
A deep convolutional neural network architecture that was introduced for the purpose of improving utilization of computing resources inside a deep neural network and reducing the number of parameters.

Inductive Reasoning
A method of reasoning in which the premises are viewed as supplying some evidence, but not full assurance, of the truth of the conclusion.

Inference Engine
The component of a knowledge-based system that applies logical rules to the knowledge base to deduce new information.

Information Retrieval
The activity of obtaining information system resources that are relevant to an information need from a collection of those resources.

Instance-Based Learning
A model of machine learning in which the algorithm doesn't learn a model per se but memorizes the training instances which are subsequently used as “knowledge” for the prediction phase.

Intelligent Agent
An autonomous entity which observes and acts upon an environment and directs its activity towards achieving goals (i.e., it is rational, as defined in economics).

Isotonic Regression
A regression algorithm that finds a non-decreasing approximation of a function while minimizing the mean squared error on the training data.​

IoT Analytics
Analysis of data generated by the Internet of Things (IoT) devices.
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Jacobian Matrix
In machine learning, particularly in neural network training, it's a matrix of all first-order partial derivatives of a vector-valued function.

Jensen-Shannon Divergence
A method of measuring the similarity between two probability distributions. It is based on the Kullback–Leibler divergence, with some modifications.

Jupyter Notebook
An open-source web application that allows the creation and sharing of documents containing live code, equations, visualizations, and narrative text, widely used in data science and machine learning.

Joint Distribution
In statistics, a probability distribution that gives the probability that each of two or more random variables takes at a particular value or range of values.

Joint Learning
A situation in machine learning where multiple tasks are learned at the same time while taking advantage of commonalities and differences across tasks.

JSON
(JavaScript Object Notation):** A lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate.
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K-Means Clustering
A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest me. A simple, versatile, and easy-to-implement supervised machine learning algorithm that can be used for classification and regression.

K-Nearest Neighbors Algorithm (KNN)
A simple, supervised machine learning algorithm that can be used for classification and regression problems, where the output is a class membership or a value, respectively.

Kappa Statistic
A statistical measure of inter-rater reliability for categorical items, often used in machine learning to measure agreement between predicted and observed categorizations.

Kernel
In machine learning, a kernel is a class of algorithms for pattern analysis, whose best-known member is the support vector machine (SVM).

Kernel Density Estimation
A non-parametric way to estimate the probability density function of a random variable.

Kernel Method
Any algorithm that depends only on the dot product between two vectors in a feature space.

Kernel Trick
A method used in machine learning algorithms to enable them to operate in a high-dimensional, implicit feature space without having to compute the coordinates of the data in that space.

Knowledge Base
In AI, a knowledge base is a centralized repository for information: a public library, a database of related information about a particular subject.

Knowledge Graph
A knowledge base that uses a graph-structured data model or topology to integrate data and in some cases derive new knowledge.

Knowledge Representation
A field of artificial intelligence dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks.
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Knowledge Distillation
A technique where a smaller, simpler model is trained to mimic a larger, more complex model to achieve similar performance with reduced computational resources.
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Lambda Architecture
A data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods.

Latent Dirichlet Allocation (LDA)
A generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

Latent Semantic Analysis (LSA)
A technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.

Latent Variable
In statistics, latent variables are variables that are not directly observed but are rather inferred from other variables that are observed and directly measured.

Latent Variables
Variables that are not directly observed but are rather inferred from other variables that are observed and directly measured.

Leaky ReLU
A variant of the rectified linear unit (ReLU), an activation function used in neural networks, that allows a small gradient when the unit is not active.

Learning Rate
In machine learning, it is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.

Linear Regression
A basic form of predictive analysis, describing the relationship between one dependent variable and one or more independent variables.

Long Short-Term Memory (LSTM)
A type of recurrent neural network used in deep learning capable of learning order dependence in sequence prediction problems.

Loss Function
In AI and machine learning, a loss function is used to quantify how far a prediction model's prediction is from the actual result.

Language Models
In natural language processing, these are statistical models that learn to predict the probability of a sequence of words in a language.

Neuroevolution
The use of evolutionary algorithms to generate artificial neural network structures and weights.
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Machine Learning
A subset of AI that includes algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions.

Markov Decision Process
A mathematical process used for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

Metaheuristic
An algorithmic framework used for finding approximate solutions to complex optimization problems, commonly used in AI for problems that are too large or complex for traditional optimization methods.

Model Fitting
The process of adjusting a machine learning model to a set of data by tuning its parameters or structure.

Model Generalization
The ability of a machine learning model to perform well on new, unseen data or scenarios.

Model Persistence
Refers to the methods used to save and load trained machine learning models for later use or analysis.

Monte Carlo Methods
A broad class of computational algorithms that rely on repeated random sampling to obtain numerical results, often used in physical and mathematical problems and for simulations in AI.

Multiclass Classification
A classification problem where there are more than two classes; each sample is labeled as being a member of one of the several possible classes.

Multi-Agent System
A system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve.

Multilayer Perceptron (MLP)
A class of feedforward artificial neural network (ANN) that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer.

Multitask Learning
A branch of machine learning in which multiple learning tasks are solved at the same time while exploiting commonalities and differences across tasks.

Model Deployment
The process of integrating a machine learning model into an existing production environment to make practical business decisions based on data.
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Naive Bayes Classifier
A simple yet effective and commonly used statistical classifier that applies Bayes' theorem with strong independence assumptions between features.

Natural Language Generation
The process of producing meaningful phrases and sentences in the form of natural language from some internal representation.
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Natural Language Processing (NLP)
A field of AI that gives machines the ability to read, understand, and derive meaning from human languages.

Natural Language Understanding (NLU)
A subtopic of natural language processing that deals with machine reading comprehension and involves the use of algorithms to understand and interpret human language.

Neural Architecture Search (NAS)
A process for automating the design of artificial neural networks, a subfield of automated machine learning.

Neural Network
A network or circuit of neurons, or in a modern sense, an artificial neural network composed of artificial neurons or nodes.

Non-Linear Regression
A form of regression analysis in which observational data is modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.

Non-Negative Matrix Factorization (NMF)
A group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements.

Normalization
A process in machine learning to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values.

Normalized Data
Data that has been processed to appear similar across all records and fields.​13.

Natural Language Generation (NLG)
The process of producing natural language text or speech from a machine representation system.
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Object Detection
A computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos.

Object Recognition
In computer vision, it's the identification of a specific object in a digital image or video, distinguished from scene classification, scene recognition, and object detection.

Ontological Engineering
A subfield of artificial intelligence that studies the methods and methodologies for building ontologies, which are formal representations of a set of concepts within a domain and the relationships between those concepts.

Ontology
In the context of computer and information sciences, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations of the concepts, data, and entities that substantiate one, many, or all domains of discourse.

Optimization Algorithms
In machine learning, these algorithms are used for minimizing (or maximizing) an objective function E(x) over some set of possible input values x.

Outlier Detection
The process of detecting and subsequently excluding outliers from a given set of data.

Out-of-Bag Error
In random forests, a method of measuring the prediction error of random forests and other ensemble learning methods by using part of the training set not used in constructing a particular tree for validation.

Overfitting
A modeling error in machine learning when a function is too closely fitted to a limited set of data points, resulting in poor predictive performance.

Overparameterization
In machine learning, it refers to models that have more parameters than can be justified by the data, leading potentially to overfitting.


Over-Sampling
A technique used to adjust the class distribution of a dataset (for example, in a dataset where class imbalance is present).
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One-Shot Learning
A machine learning paradigm where the learning algorithm is designed to learn information about object categories from one, or only a few, training samples/images.
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Online Learning
A model that learns incrementally by continuously adjusting to new data.
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PCA (Principal Component Analysis)
A statistical technique used to simplify the complexity in high-dimensional data while retaining trends and patterns.

Perceptron
A type of artificial neuron used in supervised learning of binary classifiers.

Permutation Importance
A technique used to calculate feature importance for any fitted estimator by measuring how random re-shuffling (permutation) of each feature impacts model performance.

Pooling Layer
A layer in a convolutional neural network that reduces the spatial dimensions (width and height) of the input volume for the next convolutional layer.

Precision and Recall
In pattern recognition, information retrieval, and binary classification, precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of relevant instances that were retrieved.

Predictive Analytics
The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Predictive Modeling
The process of using a statistical model to predict or forecast future outcomes based on historical data.

Probabilistic Graphical Models
A framework for encoding probability distributions over complex domains; they combine the uncertainty (probabilistic) with structural relationships (graphical models).

Probabilistic Reasoning
A type of logic that deals with reasoning with statements that are only true with a certain probability.

Principal Component Analysis (PCA)
A statistical technique used to simplify the complexity in high-dimensional data while retaining trends and patterns.

Probabilistic Reasoning
A type of logic that deals with reasoning with statements that are only true with a certain probability.

Process Automation
Refers to the use of digital technology to perform a process or processes in order to accomplish a workflow or function.

Programming Languages (AI relevant)
Languages often used in AI include Python, R, Lisp, Prolog, and Java, each offering unique frameworks and libraries suited to different AI tasks.
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Q-Learning
A model-free reinforcement learning algorithm to learn the value of an action in a particular state.

Quantile Regression
In statistics and machine learning, quantile regression aims to estimate either conditional median or other quantiles of the response variable.

Quantization (in AI)
The process of mapping input values from a large set to output values in a smaller set, often used in machine learning to optimize model size and performance.

Quantum Computing
An area of computing focused on developing computer technology based on the principles of quantum theory, which explains the nature and behavior of matter and energy on the quantum (atomic and subatomic) level.

Quantum Machine Learning
An emerging field that combines quantum physics with machine learning, involving quantum algorithms for machine learning tasks.

Quasi-Newton Methods
A type of optimization algorithm used in machine learning for finding local maxima and minima of functions.

Query Learning
In machine learning, it refers to a model or algorithm gaining information by querying a source or user, often used in scenarios where labeled data is scarce but unlabeled data is abundant.

Queue Theory
In mathematical theory, the study of queues or waiting lines, which can be applied to model various types of traffic and scheduling processes in computer systems, including those in AI applications.
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Quantum Neural Networks
Theoretical models that combine principles of quantum computing with those of artificial neural networks.

Quantum Artificial Intelligence
The application of quantum computing for the development of AI.
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Radial Basis Function (RBF)
A real-valued function whose value depends only on the distance from the origin, or alternatively on the distance from some other point called a center, often used in function approximation and time series prediction.

Radial Basis Function Network (RBFN)
A type of artificial neural network that uses radial basis functions as activation functions, often used for function approximation, time series prediction, and control.

Random Forest
An ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees at training time.

Random Forests
An ensemble learning technique that constructs multiple decision trees during training and outputs the class that is the mode of the classes or mean prediction of the individual trees.

Recurrent Neural Network (RNN)
A class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior.

Reinforcement Learning
A type of machine learning algorithm where an agent learns to behave in an environment by performing actions and seeing the results of actions, focusing on long-term rewards.

Regularization Techniques
Methods in machine learning that reduce overfitting in models by penalizing complex models.

ReLU (Rectified Linear Unit)
A type of activation function used in neural networks, particularly deep neural networks, that allows a model to solve non-linear problems.

Residual Networks (ResNets)
A type of deep neural network architecture that introduces "skip connections" or "shortcuts" to jump over some layers, helping to solve the vanishing gradient problem and enabling the training of deeper networks.

Robotics
The branch of technology that deals with the design, construction, operation, and application of robots, often incorporating AI to enable autonomous decision-making.

Rule-Based System
A set of "if-then" rules used for creating AI applications, especially for expert systems.

Recurrent Neural Networks (RNNs)
A class of neural networks where connections between units form a directed cycle, allowing them to exhibit temporal dynamic behavior.

Reinforcement Learning
A type of machine learning algorithm where an agent learns to behave in an environment by performing actions and seeing the results of actions, focusing on long-term rewards.

Recommender Systems
Systems that predict the "rating" or "preference" a user would give to an item
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Semi-Supervised Learning
A machine learning approach involving a small amount of labeled data and a large amount of unlabeled data for training, typically a middle ground between supervised and unsupervised learning.

Sentiment Analysis
A technique used in natural language processing to determine whether data is positive, negative, or neutral, often used to analyze views of a product or service.

Sequential Model
In machine learning, it's a linear stack of layers where you can create a model by passing a list of layer instances to the constructor.

Siamese Networks
A neural network architecture that learns to differentiate between two inputs. It is used to compare the similarity of inputs by comparing their feature vectors.

Softmax Function
A function that takes as input a vector of K real numbers and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers.

Softmax Regression
A generalization of logistic regression that can be used for multi-class classification, and the probabilities summing to one.

Stochastic Gradient Descent
A gradient descent optimization method for minimizing an objective function that is written as a sum of differentiable functions.

Stochastic Optimization
An optimization method that generates and uses random variables for objective function determination, constraint satisfaction, and search.

Support Vector Machine (SVM)
A supervised machine learning model that uses classification algorithms for two-group classification problems.

Supervised Learning
A type of machine learning and artificial intelligence where the model is trained on labeled data.

Swarm Intelligence
The collective behavior of decentralized, self-organized systems, natural or artificial, often used in work on artificial intelligence.
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Self-Supervised Learning
A form of unsupervised learning where the data provides the supervision. The model learns to predict part of its input from other parts.

Sparse Modeling
A process in machine learning where models are designed to use or learn only a sparse amount of their inputs or parameters.

Style Transfer
A technique in deep learning and computer vision to apply the artistic style of one image to another.
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Semantic Analysis
The process of understanding the meaning and interpretation of words, phrases, and sentences in context.
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Temporal Difference Learning
A prediction method used in machine learning, primarily in reinforcement learning, where learning to predict a quantity that depends on future values of a given signal.

Tensor Processing Unit (TPU)
An AI accelerator application-specific integrated circuit developed by Google specifically for neural network machine learning.

Time Complexity
In computational complexity theory, it's the computational complexity that describes the amount of time it takes to run an algorithm.

Time Series Analysis
The use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data.

Tokenization
In the context of data processing and natural language processing, this is the process of converting a sequence of characters into a sequence of tokens.

Topic Modeling
A type of statistical model for discovering the abstract "topics" that occur in a collection of documents, used extensively in text-mining.

Transfer Entropy
A non-parametric statistic measuring the amount of directed (time-asymmetric) transfer of information between two random processes.

Transfer Learning
A machine learning method where a model developed for a task is reused as the starting point for a model on a second task.

Transformer Model
A type of deep learning model introduced in the paper "Attention is All You Need", known for its superior performance in natural language processing tasks.

Tree-Based Models
A type of algorithm in machine learning that builds a model in the form of a tree structure, including decision trees, random forest, and gradient boosting.

Turing Test
A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

Transfer Learning
machine learning method where a model developed for one task is reused on a second, related task.
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Time Series Forecasting
The use of a model to predict future values based on previously observed values in time series data.
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Underfitting
Occurs when a statistical model or machine learning algorithm cannot adequately capture the underlying structure of the data. It usually happens when the model is too simple.

Unsupervised Learning
A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses

Utility-Based Learning
In machine learning, it's a model in which the learning algorithm is focused on maximizing a utility function, which measures the usefulness of its output.

U-Net
A type of convolutional neural network that is particularly well suited for image segmentation tasks. It was named U-Net due to its U-shaped architecture.

Unstructured Data
Any data that does not have a recognizable structure. It is unorganized and raw and can be text-heavy.

Unstructured Learning
Learning from data that has not been structured in a predefined way, often dealing with data that does not fit well into relational tables.
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Validation Set
A set of data used to assess the strength of a predictive model in statistics and machine learning; typically used to fine-tune model parameters.

Variable Importance
In machine learning, it refers to techniques used to estimate the importance of input variables used in predictive models.

Variational Autoencoder (VAE)
A type of autoencoder that provides a probabilistic manner for describing an observation in latent space, often used in generative models like images.

Variational Inference
A method in machine learning that uses statistical techniques to approximate probability densities in a lower-dimensional space.

Vector Space Model
In natural language processing and information retrieval, a model for representing text documents as vectors in a continuous vector space.

Viterbi Algorithm
A dynamic programming algorithm for finding the most likely sequence of hidden states – called the Viterbi path – that results in a sequence of observed events.

Vapnik-Chervonenkis (VC) Dimension
A measure of the capacity (complexity, expressiveness, richness, or flexibility) of a space of functions that can be learned by a given binary classification algorithm.
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Virtual Agents
AI systems that interact with users in a human-like manner, often used in customer service.
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Wasserstein Loss
In machine learning, particularly in training generative models, Wasserstein loss is a loss function that measures the distance between two probability distributions.

Weight Decay
A regularization technique, such as L2 regularization, that involves adding a penalty to the loss function to shrink the model weights during training.

Weight Initialization
In neural networks, it's the method used to assign initial values to the weights at the start of training.

Weight Regularization
A technique used in deep learning that applies penalties to the loss function during training based on the type of weight values.

Word Embedding
A type of word representation that allows words with similar meaning to have a similar representation in a vector space.

Wolpertinger Architecture
An AI architecture that combines deep learning with a k-nearest neighbors algorithm for more effective learning in complex environments.

Weak AI (Narrow AI)
AI that is designed and trained for a particular task.
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XAI (Explainable AI)
Refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by humans. It focuses on the interpretability of AI models and their decisions.

XGBoost (Extreme Gradient Boosting)
An open-source software library that provides a gradient boosting framework for various programming languages. It is widely used for its performance and efficiency in machine learning competitions and tasks.

XOR Function
The exclusive OR function, a type of logical disjunction on two operands that results in a value of true if and only if exactly one of the operands has a value of true. In neural networks, the XOR problem is a problem of using a neural network to predict the outputs of XOR logic gates given two binary inputs.

XOR Problem
In neural networks, the XOR problem is a problem of using a neural network to predict the outputs of XOR logic gates given two binary inputs. It's historically significant as it was a catalyst for the development of multi-layer networks.
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Yield Optimisation
In AI, particularly in agriculture and manufacturing, this refers to the use of machine learning models and techniques to predict the yield of a crop or production process. It involves analyzing various factors and data to improve the effectiveness and efficiency of production.

Yottabyte
A unit of information or computer storage equal to one septillion bytes. As AI and big data continue to advance, the amount of data processed and stored is reaching into the yottabytes. This term is significant in the context of big data and AI due to the massive volumes of data that these fields often work with.

Yellow Box (in AI)
Refers to AI systems that are designed to be interpretable and transparent, providing insights into the decision-making process. These systems aim to be more user-friendly and understandable, especially in comparison to "black box" models where the decision-making process is not visible.​
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Zero-Day Attack
A cyber-attack that occurs on the same day a weakness is discovered in software. In AI, systems are increasingly being developed to predict and defend against such attacks. It involves using machine learning algorithms to identify and react to new threats as they emerge.

Zero-Knowledge Proof
A method by which one party (the prover) can prove to another party (the verifier) that they know a value x, without conveying any information apart from the fact that they know the value x. This concept is relevant in AI for secure computations and privacy-preserving data sharing.

Zero-Shot Learning
The ability of a model to solve a task despite having received no training examples for that specific task. It's an approach in machine learning where the model attempts to correctly make predictions for classes that it has not explicitly seen during training.

Z-Score Normalization
Also known as standard score, it's a statistical technique used in machine learning to normalize the features of a dataset. By converting different scales to a common scale, the Z-score normalization ensures that each feature contributes equally to the final prediction.

Zettabyte
A unit of digital information storage used to describe data capacity. One zettabyte is equal to a billion terabytes. It's relevant in AI in terms of the massive amounts of data processed and stored.

Zeroth
In AI, this often refers to the Zeroth Law of Robotics, a principle that regards the welfare of humanity as a whole over individuals. It's a theoretical extension of Isaac Asimov's Three Laws of Robotics, used to provoke discussions about the ethical implications of AI.
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