Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that...

In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability...

A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention mechanism, proposed in the 2017...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

related to transductive learning algorithms. Another example of an algorithm in this category is the Transductive Support Vector Machine (TSVM). A third possible...

open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms...

In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters...

Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of...

In machine learning, the term tensor informally refers to two different concepts for organizing and representing data. Data may be organized in a multidimensional...

Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...

Machine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning...

In machine learning, grokking, or delayed generalization, is a transition to generalization that occurs many training iterations after the interpolation...

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update...

Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value...

Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems...

In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and...

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination...

AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single...