The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence...

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

Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three...

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...

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

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...

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)...

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year....

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

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...

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

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

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

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

reinforcement learning feedback from humans and AI for human alignment and policy compliance.: 2  Observers reported that the iteration of ChatGPT using...

transformer – Machine learning model for vision processing Large language model – Type of machine learning model BERT (language model) – Series of language...

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...

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

founder of the Machine Intelligence Research Institute Glossary of artificial intelligence List of emerging technologies Outline of machine learning Artificial...

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...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory...

speech processing[citation needed]. Language modeling Transformer (machine learning model) State-space model Recurrent neural network The name comes from...

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into...

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...

language processing by machines. It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able...

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether...