Machine Learning Glossary
Key Terminology in Machine Learning Journal

Machine Learning Glossary

Key Terminology in Machine Learning

machine learning terminology

Find below the Machine learning terminology or Machine learning glossary of terms journal to help readers understand common terms in machine learning and artificial intelligence (AI), statistics, and data mining. Here we provide a glossary/terminology of common terms journal.

The machine learning definitions are not designed to be completely general, but instead are aimed at the most common case/issue on applications of machine learning and the knowledge discovery process.

AI acceleratorAi accelerator coprocessor designed to accelerate artificial neural networks, machine vision and other machine learning algorithms for robotics, internet of things and other
Action model learningAction model learning (sometimes abbreviated action learning) is an area of machine learning concerned with creation and modification of software agent's
Active learningActive learning is about a machine learning method. For active learning in the context of education, see active learning. Active learning is a special case of
Adobe eLearning SuiteCommunication Suite Adobe eLearning Suite FAQ Authorware FAQ Archived July 6, 2011, at the Wayback Machine. Captivate 5 and Adobe eLearning Suite 2 launch announcement:
Adversarial machine learningAdversarial machine learning is a research field that lies at the intersection of machine learning and computer security. It aims to enable the safe adoption
Agent mininginterdisciplinary area that synergizes multiagent systems with data mining and machine learning. The interaction and integration between multiagent systems and data
AlchemyAPIAlchemyAPI is a company that uses machine learning (specifically, deep learning) to do natural language processing (specifically, semantic text analysis
Algorithm Selectioncost-sensitive hierarchical clustering (CSHC) in machine learning, algorithm selection is better known as meta-learning. The portfolio of algorithms consists of machine learning algorithms
Algorithmic learning theoryAlgorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and
Andrew McCallumUniversity of Massachusetts Amherst. His primary specialties are in machine learning, natural language processing, information extraction, information integration
Andrew Ngan online education platform. Ng researches primarily in machine learning and deep learning. His early work includes the Stanford Autonomous Helicopter
Apache Mahoutproduce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering
Apache Singaalgorithms for machine learning models, e.g., layers for neural network models, optimizers/initializer/metric/loss for general machine learning models.
Apache Sparkthe class of iterative algorithms are the training algorithms for machine learning systems, which formed the initial impetus for developing Apache Spark
Apprenticeship learningApprenticeship learning, or apprenticeship via inverse reinforcement learning (AIRP), is a concept in the field of artificial intelligence and machine learning, developed
Apptekhuman language technology (automatic speech recognition, machine translation, NLP, machine learning and artificial intelligence), headquartered in McLean
ArffAircraft Rescue and Firefighting (ARFF) Attribute-Relation File Format (ARFF), an input file format used by the machine learning tool Weka (machine learning)
Arthur SamuelHe coined the term
Artificial intelligencewhen a machine mimics
Artificial intelligence marketingdatabase marketing techniques as well as AI concept and model such as machine learning and Bayesian Network. The main difference resides in the reasoning
Artificial neural networkpublication of machine learning research by Marvin Minsky and Seymour Papert (1969), who discovered two key issues with the computational machines that processed
Association rule learningfilmmaking technique, see Long take. Association rule learning is a rule-based machine learning method for discovering interesting relations between variables
Auditory learning(2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review Archived December 5, 2008, at the Wayback Machine.. London:
Augmented learningReality Architecture for Learning Augmented Reality Archived April 7, 2014, at the Wayback Machine. Computer Augmented Learning: The Basis of Sustained
Autoencoderbelief network. Representation learning Restricted Boltzmann machine Sparse dictionary learning Bengio, Y. (2009).
Avrim Blumcomputer science, with particular activity in the fields of machine learning, computational learning theory, algorithmic game theory, and algorithms. Avrim
Ayasdithe target data set, runs many different unsupervised and supervised machine learning algorithms on the data, automatically finds and ranks best fits, and
BackpropagationAI portal Machine learning portal Artificial neural network Biological neural network Catastrophic interference Ensemble learning AdaBoost Overfitting
Bag-of-words modelsome problems. (For instance, this option is implemented in the WEKA machine learning software system.) Bag-of-word model is an orderless document representation-only
Balabitthe development of IT security systems and related services using machine learning to secure risky privileged accounts. Balabit was founded in 2000
Barney Pellknowledge management, machine learning, artificial intelligence, and scheduling systems. In computer game playing and machine learning, he was a pioneer in
Bayes error ratefinds important use in the study of patterns and machine learning techniques. In terms of machine learning and pattern classification, the labels of a set
Bayesian optimizationincluding learning to rank, interactive animation, robotics, sensor networks, automatic algorithm configuration, automatic machine learning toolboxes
Ben Taskar- November 18, 2013) was a professor and researcher in the area of machine learning and applications to computational linguistics and computer vision.
Bernhard ScholkopfDepartment of Empirical Inference. He is a leading researcher in the machine learning community, where he is particularly active in the field of kernel methods
Bias-variance tradeoffIn statistics and machine learning, the bias-variance tradeoff (or dilemma) is the problem of simultaneously minimizing two sources of error that prevent
Boltzmann machinediscussed below, Boltzmann machines with unconstrained connectivity have not proven useful for practical problems in machine learning or inference, but if the
Boostinga machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms
Bootstrap aggregatingcalled bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical
Brendan FreyCanadian-born machine learning and genome biology researcher, known mainly for his work on factor graphs, the wake-sleep algorithm for deep learning, and using
BrownBoostboosting algorithms, BrownBoost is used in conjunction with other machine learning methods. BrownBoost was introduced by Yoav Freund in 2001. AdaBoost
CARTClassification and regression tree, a type of decision tree learning, used in machine learning, data mining and predictive analytics Clermont Area Rural
CBCLhow the human brain works and for making intelligent machines. CBCL studies the problem of learning within a multidisciplinary approach. Its main goal is
CIML community portalThe computational intelligence and machine learning (CIML) community portal is an international multi-university initiative. Its primary purpose is to
CNTKThe Microsoft Cognitive Toolkit, or previously known as CNTK, is a deep learning framework developed by Microsoft Research. Microsoft Cognitive Toolkit
Cengage LearningCengage Learning, Inc. is an educational content, technology, and services company for the higher education, K-12, professional, and library markets worldwide
Chaos Machinestunts, and generally have a good time learning and problem-solving. Over time more sets are added, so the machine grows larger with each appearance. Recently
Chih-Jen Linin machine learning, optimization, and data mining. He is best known for the open source library LIBSVM, an implementation of support vector machines.
Christopher G. Atkesonwork in humanoid robots, soft robotics, and machine learning, most notably on locally weighted learning. Atkeson graduated summa cum laude from Harvard
Classifier chainsClassifier chains is a machine learning method for problem transformation in multi-label classification. It combines the computational efficiency of the
Claude Sammuta member of the editorial boards of the Journal of Machine Learning Research, the Machine Learning Journal and New Generation Computing. He was the program
Cluster hypothesisIn machine learning and information retrieval, the cluster hypothesis is an assumption about the nature of the data handled in those fields, which takes
Collective Tuning Initiativeand machine-learning techniques and improve the quality and reproducibility of the compiler (and architecture research) Online machine learning-based
Committee machineA committee machine is a type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts)
Comparison of deep learning softwarememory model Deep learning#Software libraries List of datasets for machine learning research Comparison of datasets in machine learning Comparison of numerical
Comparison of deep learning software/ResourcesDistributed Deep LEarning
Computational learning theoryof machine learning algorithms. Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In
Computer-assisted language learningvideo game publisher, see English Software. Computer-assisted language learning (CALL) is succinctly defined in a seminal work by Levy (1997: p.1) as
Concept driftIn predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is trying
Concept learningcontain concept-relevant features. Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by
Conceptual clusteringConceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980s. It is distinguished from ordinary
Conference on Neural Information Processing Systemsand Workshop on Neural Information Processing Systems (NIPS) is a machine learning and computational neuroscience conference held every December. The
Confusion matrixIn the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific
ConstructivismConstructing Learning for as well as with others,
Contemporary Learning CenterOverview,
Corinna CortesCortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is
Coupled pattern learnerCoupled Pattern Learner (CPL) is a machine learning algorithm which couples the semi-supervised learning of categories and relations to forestall the
Dalle Molle Institute for Artificial Intelligence Researchinstitute won eight international competitions in pattern recognition and machine learning. IDSIA is one of four Swiss research organisations founded by the Dalle
Dan Rothof which is the idea that learning has a central role in intelligence. His work centers around the study of machine learning and inference methods to facilitate
Dana Ron73-205, 2009. D. Ron. Property Testing: A Learning Theory Perspective, Foundations and Trends in Machine Learning: vol. 1, no. 3, pages 307-402, 2008. N
Data analysis techniques for fraud detectionrely on systems that have been based around machine learning, rather than later incorporating machine learning into an existing system. These companies include
Data classificationclassification (business intelligence) Classification (machine learning), classification of data using machine learning algorithms Assigning a level of sensitivity
Data mininginvolving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield
Data pre-processing
Data stream miningclass membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples
David BleiComputer Science at Princeton University. His work is primarily in machine learning. His research interests include topic models and he was one of the
David Cournapeauis the original author of the scikit-learn package, an open source machine learning library in the Python programming language. He works as a developer
David E. Goldbergand rule learning, PhD thesis. University of Michigan. Ann Arbor, MI. 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley
Decision listvariable or its negation. Ronald L. Rivest (Nov 1987).
Decision stumpA decision stump is a machine learning model consisting of a one-level decision tree. That is, it is a decision tree with one internal node (the root)
Decision treetrees in decision analysis. For the use of the term in machine learning, see Decision tree learning. A decision tree is a decision support tool that
Decision tree learningabout decision trees in machine learning. For the use of the term in decision analysis, see Decision tree. Decision tree learning uses a decision tree as
Declaraplatform learns how users interact through the use of semantic search, machine learning algorithms, and recommendations to deliver personalized suggestions
Deep belief networkIn machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple
Deep feature synthesis(ed.), European Working Session on Learning, Porto, Portugal, March 6-8, 1991 ; Y. Kodratoff (1991). Machine learning--EWSL-91 : proceedings. Berlin: Springer-Verlag
Deep learningstructured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model
Deeplearning4jdeep learning programming library written for Java and the Java virtual machine (JVM) and a computing framework with wide support for deep learning algorithms
Developmental roboticsallow lifelong and open-ended learning of new skills and new knowledge in embodied machines. As in human children, learning is expected to be cumulative
Dimensionality reductionFor dimensional reduction in physics, see Dimensional reduction. In machine learning and statistics, dimensionality reduction or dimension reduction is
Discretization of continuous featuresIn statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to
Distributed Rdata loaders. It is mostly used to implement distributed versions of machine learning tasks. Distributed R is written in C++ and R, and retains the familiar
Dlibcreating a broad set of statistical machine learning tools and in 2009 dlib was published in the Journal of Machine Learning Research. Since then it has been
Domain adaptationAdaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning from a source data distribution a
Drew Conwaybook Machine Learning for Hackers, a book of case studies that illustrates data science from a hacker's perspective. He is also co-author of Machine Learning
E-learningE-learning theory describes the cognitive science principles of effective multimedia learning using electronic educational technology. Cognitive research
EBLEstimated blood loss European Bridge League Explanation-based learning, a form of machine learning Exploits Block List Extragalactic background light EBL of
ECML PKDDon Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is one of the leading academic conferences on machine learning and
Eager learningIris; Van den Bosch, Antal (October 2005).
Early stoppingIn machine learning, early stopping is a form of regularization used to avoid overfitting when training a learner with an iterative method, such as gradient
Educational technology
Empirical risk minimizationstatistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on the performance of learning algorithms
EncogEncog is a machine learning framework available for Java, .Net, and C++. Encog supports different learning algorithms such as Bayesian Networks, Hidden
Ensembleaveraging (machine learning) Distribution ensemble or probability ensemble (cryptography) Ensemble learning (statistics and machine learning) Neural ensemble
Ensemble averagingto be confused with Ensemble averaging (statistical mechanics). In machine learning, particularly in the creation of artificial neural networks, ensemble
Ensemble learningvariational Bayesian methods. In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance
Ensembles of classifiersRecently in the area of machine learning the concept of combining classifiers is proposed as a new direction for the improvement of the performance of
Eric XingXing is a professor at Carnegie Mellon University and researcher in machine learning, computational biology, and statistical methodology. Xing received
Error Toleranceintelligence portal Machine learning portal Machine learning Data mining Probably approximately correct learning Adversarial machine learning Valiant,
Error-driven learningError-driven learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to minimize some error
Evolving classification functionclassifiers are used for classifying and clustering in the field of machine learning and artificial intelligence, typically employed for data stream mining
Example-based machine translationimplementation of a case-based reasoning approach to machine learning. At the foundation of example-based machine translation is the idea of translation by analogy
Expectation propagationExpectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative approach
Explanation-based learningExplanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory to make generalizations or form
Extreme learning machineExtreme learning machines are feedforward neural network for classification or regression with a single layer of hidden nodes, where the weights connecting
FeatureIn machine learning and pattern recognition, a feature is an individual measurable property of a phenomenon being observed. Choosing informative, discriminating
Feature Selection ToolboxSelection Toolbox (FST) is software primarily for feature selection in the machine learning domain, written in C++, developed at the Institute of Information Theory
Feature engineeringcreate features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult
Feature hashingIn machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that learn a feature: a transformation of raw data input to a
Feature scalingstep. Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization
Feature vectorredirects here. For feature spaces in kernel machines, see Kernel method. In pattern recognition and machine learning, a feature vector is an n-dimensional vector
Features from accelerated segment test(DoG) used by the SIFT, SUSAN and Harris detectors. Moreover, when machine learning techniques are applied, superior performance in terms of computation
Feedzaidetects fraud in omnichannel commerce. The company uses real-time, machine-based learning to analyze big data to identify fraudulent payment transactions
Finite-state machine
First Order Inductive LearnerIn machine learning, First Order Inductive Learner (FOIL) is a rule-based learning algorithm. Developed in 1990 by Ross Quinlan, FOIL learns function-free
Foster ProvostEditor-in-Chief of the journal Machine Learning after 6 years. He is a member of the editorial boards of the Journal of Machine Learning Research (JMLR) and the
Gaussian processcontinuous domain, e.g. time or space. Viewed as a machine-learning algorithm, a Gaussian process uses lazy learning and a measure of the similarity between points
Generalization errorIn supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error) is
Geoff Webbassociation rule learning. His early work included advocating the use of machine learning to create black box user models; interactive machine learning; and one
Geoffrey Hintonat the University of Toronto. He holds a Canada Research Chair in Machine Learning. He is the director of the program on
Geometric feature learningGeometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find
GloVeGloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence
Glossary of artificial intelligenceprogramming - Machine vision - Markov chain - Markov decision process - Mathematical optimization - Machine learning - Machine listening - Machine perception
Google BrainAndrew Ng (26 June 2012).
Google Neural Machine Translationbased (EBMT) machine translation method in which the system
Gordon HunterReinforcement Learning, Sports Analysis, Statistical Learning and Modelling. His current research interests include machine learning applications in
Grammar inductionin machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite state machine or
Graph kernelarticle is about machine learning. For the graph-theoretical notion, see Glossary of graph theory. In structure mining, a domain of learning on structured
GraphLabusing an Apache License. While GraphLab was originally developed for Machine Learning tasks, it has found great success at a broad range of other data-mining
Graphical modelprobability theory, statistics-particularly Bayesian statistics-and machine learning. Generally, probabilistic graphical models use a graph-based
Godel machinemathematical theories. The Godel machine is often discussed when dealing with issues of meta-learning, also known as
H2Oof Arno Candel; after H2O was rated as the best
HPCC Systems
Hartmut Nevenwork in face and object recognition and his contributions to quantum machine learning. He is currently Director of Engineering at Google where he is leading
Helmholtz machineitself. Helmholtz machines are usually trained using an unsupervised learning algorithm, such as the wake-sleep algorithm. Helmholtz machines may also be used
Hierarchical temporal memorytemporal memory (HTM) is an unsupervised to semi-supervised online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc.
Higher LearningFor other uses, see Higher Learning (disambiguation). Higher Learning is a 1995 American drama film written and directed by John Singleton, and starring
Hinge lossIn machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for
Hug machine
Hype MachineVolodkin: Why Steep Learning Curves Are Worth It The 99 Percent. Bloggers' banquet: For new and old music believe the Hype Machine The Guardian. March
Hyperparameter optimizationhyperparameters in machine learning. For hyperparameters in Bayesian statistics, see Hyperparameter. In the context of machine learning, hyperparameter optimization
IEEE Transactions on Pattern Analysis and Machine Intelligenceimage understanding, pattern analysis and recognition, and machine intelligence. machine learning, search techniques, document and handwriting analysis, medical
Ian Goodfellowworking in machine learning, currently employed as a research scientist at OpenAI. He has made several contributions to the field of deep learning. Goodfellow
Ilya SutskeverIlya Sutskever is a Computer Scientist working in Machine Learning and currently serving as the research director of OpenAI. Sutskever obtained his B.S
Incremental decision treeAn incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5,
Incremental learningIncremental learning is a method of machine learning algorithms, which input data is been reading consecutively and is used to extend the existing model
Inductive biasIn machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. To achieve this, the learning algorithm
Inductive programmingprograms but on machine learning of symbolic hypotheses from logical representations. However, there were some encouraging results on learning recursive Prolog
Inductive transferInductive transfer, or transfer learning, is a research problem in machine learning that focuses on storing knowledge gained while solving one problem
Inferential theory of learningInferential theory of learning (ITL) is an area of machine learning which describes inferential processes performed by learning agents. ITL has been developed
Instance-based learningIn machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit
Instantaneously trained neural networksProjection and for deep learning, International Conference on Machine Learning and Cybernetics, Dalin, 2006 Schmidhuber, J. Deep Learning in Neural Networks:
Intelligent controlapproaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. Intelligent control
International Conference on Machine LearningInternational Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting annually more than
International Meeting on Computational Intelligence Methods for Bioinformatics and BiostatisticsBiostatistics (CIBB) is a preeminent yearly conference focused on machine learning and computational intelligence applied to bioinformatics and biostatistics
Jaime Carbonelland related tasks. He also works on machine translation, both high-accuracy knowledge-based MT and machine learning for corpus-based MT (such as generalized
Jeremy Howardand entrepreneur. He is the CEO and Founder at Enlitic, an advanced machine learning company in San Francisco, California. Previously, Howard was the President
John D. LaffertyBlock Professor at the University of Chicago and leading researcher in machine learning. He is best known for proposing the Conditional Random Fields with
John LangfordJohn Langford, see John Langford (disambiguation). John Langford is a machine learning research scientist, a field which he says
John Shawe-TaylorComputational Statistics and Machine Learning at University College, London (UK). His main research area is statistical learning theory. He has written with
Journal of Machine Learning ResearchThe Journal of Machine Learning Research (usually abbreviated JMLR), is a scientific journal focusing on machine learning, a subfield of artificial intelligence
JubatusJubatus is an open source online machine learning and distributed computing framework that is developed at Nippon Telegraph and Telephone and Preferred
K-means clusteringloose relationship to the k-nearest neighbor classifier, a popular machine learning technique for classification that is often confused with k-means because
K-nearest neighbors algorithmuntil classification. The k-NN algorithm is among the simplest of all machine learning algorithms. Both for classification and regression, it can be useful
KNIMEand integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical
Kernel methodIn machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general
Kernel methods for vector outputalgorithms to easily swap functions of varying complexity. In typical machine learning algorithms, these functions produce a scalar output. Recent development
Kernel perceptronIn machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers
Kernel random forestIn machine learning, kernel random forests establish the connection between random forests and kernel methods. By slightly modifying their definition,
Klaus-Robert MüllerGerman physicist and computer scientist, most noted for his work in Machine Learning and Brain-Computer Interfaces. Klaus-Robert Müller received his Diplom
Knitting machinetime but does require learning to operate the machines correctly. Most if not all hand knitting patterns can be worked up on a machine, either identically
Knowledge Engineering and Machine Learning GroupThe Knowledge Engineering and Machine Learning group (KEMLg) is a research group belonging to the Technical University of Catalonia (UPC) - BarcelonaTech
Knowledge integrationexploiting these learning opportunities the learning agent is able to learn beyond the explicit content of the new information. The machine learning program KI
Krzysztof CiosUniversity (VCU), located in Richmond, Virginia. His research is focused on machine learning, data mining, and biomedical informatics. Krzysztof J. Cios, a Polish-American
LIBSVMLIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though
LIONsolvera software system is running. Learning and Intelligent OptimizatioN refers to the integration of online machine learning schemes into the optimization
Large margin nearest neighbornearest neighbor (LMNN) classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor
Larry A. Wassermanstatistician and a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University. Wasserman received his
Lazy learningIn machine learning, lazy learning is a learning method in which generalization beyond the training data is delayed until a query is made to the system
Learnable evolution modelnon-Darwinian methodology for evolutionary computation that employs machine learning to guide the generation of new individuals (candidate problem solutions)
Learnable function classregularization in machine learning, and provides large sample justifications for certain learning algorithms. See also: Statistical learning theory Let
Learningpossessed by humans, animals, plants and some machines. Progress over time tends to follow a learning curve. Learning does not happen all at once, but it builds
Learning Tree InternationalLearning Tree International Inc. (OTCQX: LTRE), is an American multinational training company that has provided skills-enhancement training to over 2.4
Learning analyticsLearning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and
Learning automataof regular languages. Learning automata is one type of Machine Learning algorithm studied since 1970s. Compared to other learning scheme, a branch of the
Learning classifier systemLearning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic
Learning curveFor other uses, see Learning curve (disambiguation). A learning curve is a graphical representation of the increase of learning (vertical axis) with experience
Learning object metadataLearning Object Metadata is a data model, usually encoded in XML, used to describe a learning object and similar digital resources used to support learning
Learning rulemain models of machine learning: Unsupervised learning Supervised learning Reinforcement learning Machine learning Decision tree learning Pattern recognition
Learning theorya mathematical theory to analyze machine learning algorithms. Online machine learning, the process of teaching a machine. Statistical learning theory
Learning to rankLearning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning
Learning vector quantizationfor the WEKA Machine Learning Workbench. GMLVQ toolbox: An easy-to-use implementation of Generalized Matrix LVQ (matrix relevance learning) in (c) matlab
Learning with errorsLearning with errors (LWE) is a problem in machine learning that is conjectured to be hard to solve. Introduced by Oded Regev in 2005, it is a generalization
Leo Breimanbetween statistics and computer science, particularly in the field of machine learning. His most important contributions were his work on classification and
Leslie Valiant
Linear separabilityhyperplane if they are arises in several areas. In statistics and machine learning, classifying certain types of data is a problem for which good algorithms
Lise GetoorUniversity of Maryland, College Park. Her primary research interests are in machine learning and reasoning with uncertainty, applied to graphs and structured data
List of artificial intelligence projectsprocessing, speech recognition, machine vision, probabilistic logic, planning, reasoning, many forms of machine learning) into an AI assistant that learns
List of datasets for machine learning researchof the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware
List of machine learning conceptscorrect learning (PAC) learning Ripple down rules, a knowledge acquisition methodology Symbolic machine learning algorithms Support vector machines Random
List of programming languages for artificial intelligenceSmalltalk has been used extensively for simulations, neural networks, machine learning and genetic algorithms. It implements the purest and most elegant form
List of statistical packagesTorch (machine learning) - a deep learning software library written in Lua (programming language) Weka (machine learning) - a suite of machine learning software
Logic learning machineLogic Learning Machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching
Logistic model treeLandwehr, N.; Hall, M.; Frank, E. (2005).
LogitBoostIn machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani
Louis-Philippe Morencya French Canadian researcher interested in human communication and machine learning applied to a better understanding of human behavior. Dr. Louis-Philippe
Lyle UngarLyle H. Ungar is a machine learning researcher and professor of Computer and Information Science at the University of Pennsylvania, and is also affiliated
Leon Bottouknown for his work in machine learning and data compression. His work presents stochastic gradient descent as a fundamental learning algorithm. He is also
M-Theoryarticle is about machine learning. For the physics term, see M-theory. In Machine Learning and Computer Vision, M-Theory is a learning framework inspired
MILEPOST GCCstable production-quality GCC, Interactive Compilation Interface and machine learning plugins to adapt to any given architecture and program automatically
MLPACKmlpack is a machine learning software library for C++, built on top of the Armadillo library. mlpack has an emphasis on scalability, speed, and ease-of-use
MNIST databasedatabase is also widely used for training and testing in the field of machine learning. It was created by
Machineperform tasks. For other uses, see Machine (disambiguation). Further information: Equipment (disambiguation) A machine is a tool containing one or more
Machine LearningMachine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning
Machine learningFor the journal, see Machine Learning (journal). Machine learning is the subfield of computer science that gives computers the ability to learn without
Machine listeninganalysis, filtering, and audio transforms); artificial intelligence (machine learning and sound classification); psychoacoustics (sound perception); cognitive
Machine translationwhen translating. Main article: Neural machine translation A deep learning based approach to MT, neural machine translation has made rapid progress in
Machine-dependent softwareand Xie, M., 2015, An empirical analysis of data preprocessing for machine learning-based software cost estimation, Information and Software Technology
MalletMALLET is a Java
Manifold alignmentManifold alignment is a class of machine learning algorithms that produce projections between sets of data, given that the original data sets lie on a
Manifold regularizationIn machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that
Mann & MachineMann and Machine is an American science fiction/police drama series that aired for nine episodes on NBC from April 5 to July 14, 1992. Created by Dick
MarginIn machine learning the margin of a single data point is defined to be the distance from the data point to a decision boundary. Note that there are many
Margin classifierIn machine learning, a margin classifier is a classifier which is able to give an associated distance from the decision boundary for each example. For
Massive Online Analysisopen-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners
Matrix regularizationfeature learning. Machine Learning, 73(3):243-272, 2008. Huang, Zhang, and Metaxas. Learning with Structured Sparsity. Journal of Machine Learning Research
MatrixNetMatrixNet is a proprietary machine learning algorithm developed by Yandex and used widely throughout the company products. The algorithm is based on gradient
Mehryar MohriMathematical Sciences at New York University known for his work in machine learning, automata theory and algorithms, speech recognition and natural language
Metafor SoftwareUsing machine learning techniques inspired by how the human brain works, Metafor detects unexpected changes and behavioral anomalies in Machine-generated
Michael Collinsprocessing as well as machine learning and he has made important contributions in statistical parsing and in statistical machine learning. In his studies Collins
Michael I. Jordanat the University of California, Berkeley and leading researcher in machine learning and artificial intelligence. Jordan received his BS magna cum laude
Michael Kearnsleading researcher in computational learning theory and algorithmic game theory, and interested in machine learning, artificial intelligence, computational
Michael L. Littmancomputer scientist. He works mainly in reinforcement learning, but has done work in machine learning, game theory, computer networking, partially observable
Microsoft AzureState Configuration.[1] Microsoft SMA (software) Microsoft Azure Machine Learning (Azure ML) service is part of Cortana Intelligence Suite that enables
Microsoft ResearchHardware and Devices Health and Well-being Human-computer interaction Machine learning and Artificial intelligence Mobile computing Quantum computing Search
Mike Phillipswriter Mike Phillips (speech recognition) (born 1961), pioneer in machine learning and speech recognition Michael Brandon (pornographic actor) (born 1965)
Mind machineA mind machine (aka brain machine or light and sound machine) uses pulsing rhythmic sound, flashing light, electrical or magnetic fields, or a combination
Mlpymlpy is a Python, open source, machine learning library built on top of NumPy/SciPy, the GNU Scientific Library and it makes an extensive use of the Cython
Multi-armed banditexpected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in reinforcement learning. The multi-armed bandit
Multi-task learningMulti-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities
Multiclass classificationNot to be confused with multi-label classification. In machine learning, multiclass or multinomial classification is the problem of classifying instances
Multilayer perceptronpopular machine learning solution in the 1980s, finding applications in diverse fields such as speech recognition, image recognition, and machine translation
Multilinear subspace learningsubspace learning for tensor data (open access version). Lecture: Video lecture on UMPCA at the 25th International Conference on Machine Learning (ICML 2008)
Multiple instance learningdata, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple
Multiple kernel learningMultiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination
Multiple-instance learningIn machine learning, multiple-instance learning (MIL) is a variation on supervised learning. Instead of receiving a set of instances which are individually
Music information retrievalin musicology, psychology, academic music study, signal processing, machine learning or some combination of these. MIR is being used by businesses and
Nando de Freitasfield of machine learning, and in particular in the subfields of neural networks, Bayesian inference and Bayesian optimization, and deep learning. De Freitas
Natural language processingNLP algorithms are based on machine learning, especially statistical machine learning. The paradigm of machine learning is different from that of most
Nello Cristianinianalysis; machine learning and artificial intelligence; machine translation; bioinformatics. As a practitioner of data-driven AI and Machine Learning, Cristianini
Nest Learning Thermostatcooling of homes and businesses to conserve energy. It is based on a machine learning algorithm: For the first weeks users have to regulate the thermostat
Neural DesignerNeural Designer is a software tool for data mining based on machine learning techniques, a main area of artificial intelligence research. It has been developed
Neural machine translationNeural machine translation (NMT) is an approach to machine translation in which a large neural network is trained by deep learning techniques. It is a
Never-Ending Language LearningNever-Ending Language Learning system (NELL) is a semantic machine learning system developed by a research team at Carnegie Mellon University, and supported
Nir FriedmanHebrew University of Jerusalem. His research combines Machine Learning and Statistical Learning with Systems Biology, specifically in the fields of Gene
Novelty detectionthat a machine learning system has not been trained with and was not previously aware of, with the help of either statistical or machine learning based
NumentaNuPIC is to build and support a community, that is interested in machine learning and machine intelligence based on modeling the neocortex and its principles
Observational learninglearning (disambiguation). Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which
Occam learningIn computational learning theory, Occam learning is a model of algorithmic learning where the objective of the learner is to output a succinct representation
Ofer Dekelsee Ofer Dekel. Ofer Dekel is a computer science researcher in the Machine Learning Department of Microsoft Research. He obtained his PhD in Computer Science
Offline learningIn machine learning, systems which employ offline learning do not change their approximation of the target function when the initial training phase has
Omniscien Technologiesstatistical and / or neuronal techniques from cryptography, applying machine learning algorithms that automatically acquire statistical models from existing
One-class classificationIn machine learning, one-class classification, also known as unary classification, tries to identify objects of a specific class amongst all objects, by
Online(ONLine), proposed electrical power line in Nevada Online (machine learning) a method of machine learning in which data becomes available in a sequential order
Online learningOnline learning may refer to E-learning, in education E-learning (theory) Online machine learning, in computer science and statistics Online learning in
Online machine learningIn 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
OpenNNDesigner, also developed by Artelnics Artificial intelligence Machine learning Deep learning Artificial neural network
Orangeother uses, see Orange (disambiguation). Orange is a free software machine learning and data mining package (written in Python). It has a visual programming
Ordinal regressionas well as in information retrieval. In machine learning, ordinal regression may also be called ranking learning. Ordinal regression can be performed
Osmar R. Zaianeprofessor at the University of Alberta specializing in data mining and machine learning. He was the secretary treasurer of the Association for Computing Machinery
Out-of-bag errorprediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating to sub-sample data sampled used
Outline of machinestopical guide to machines: Machine - mechanical system that provides the useful application of power to achieve movement. A machine consists of a power
OverfittingIn statistics and machine learning, one of the most common tasks is to fit a
Oxford-Man Instituteapplications of machine learning and data analytics. The current Director of the Oxford-Man Institute is Stephen Roberts, a Professor of Machine Learning in Information
Pachinko allocationIn machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover
Padhraic SmythScience Initiative, and Associate Director for UC Irvine's Center for Machine Learning and Intelligent Systems.
Parity learningParity learning is a problem in machine learning. An algorithm that solves this problem must guess the function ƒ, given some samples (x, ƒ(x)) and the
Pattern recognitionThis article is about pattern recognition as a branch of machine learning. For the cognitive process, see Pattern recognition (psychology). For other uses
Pedro DomingosDomingos is Professor at University of Washington. He is a researcher in machine learning and known for markov logic network enabling uncertain inference.
Perceptron1969 book, see Perceptrons (book). In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can
Perceptual learningPerceptual learning is the process of learning improved skills of perception. These improvements range from simple sensory discriminations (e.g., distinguishing
Personalized learningPersonalized learning, individualized instruction, personal learning environment and direct instruction all refer to efforts to tailor education to meet
Peter DayanComputational Neuroscience). He is known for applying Bayesian methods from Machine Learning and Artificial Intelligence to understand neural function, and is particularly
Peter RichtarikSlovak mathematician working in the area of big data optimization and machine learning, known for his work on randomized coordinate descent algorithms. He
Pierre BaldiPierre Baldi's research include artificial intelligence, statistical machine learning, and data mining, and their applications to problems in the life sciences
Platt scalingIn machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution
Plyused the term in its game-theoretic sense in his seminal paper on machine learning in checkers in 1959. In computing, the concept of ply is important
Polynomial kernelThis article is about machine learning. For polynomial kernels in complexity theory, see Kernelization. In machine learning, the polynomial kernel
Population-based incremental learningIn computer science and machine learning, population-based incremental learning (PBIL) is an optimization algorithm, and an estimation of distribution
Predictive learningPredictive learning is a technique of machine learning in which an agent tries to build a model of its environment by trying out different actions in
Preference learningPreference learning is a subfield in machine learning in which the goal is to learn a predictive preference model from observed preference information
Prior knowledge for pattern recognitionrecognition is a very active field of research intimately bound to machine learning. Also known as classification or statistical classification, pattern
Prismaticfor various Web browsers and mobile devices running iOS. It combined machine learning, user experience design, and interaction design to create a new way
Proactive Discovery of Insider Threats Using Graph Analysis and Learningis for two years and the budget $9 million. It uses graph theory, machine learning, statistical anomaly detection, and high-performance computing to scan
Proactive learninglearning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications.
Probabilistic classificationIn machine learning, a probabilistic classifier is a classifier that is able to predict, given a sample input, a probability distribution over a set of
Probably approximately correct learningcomputational learning theory, probably approximately correct learning (PAC learning) is a framework for mathematical analysis of machine learning. It was proposed
Product of expertsProduct of experts (PoE) is a machine learning technique. It models a probability distribution by combining the output from several simpler distributions
Programmed learningof applied psychologists and educators. The learning material is in a kind of textbook or teaching machine or computer. The medium presents the material
PruningPruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that provide little power to classify
Q-learningQ-learning is a model-free reinforcement learning technique. Specifically, Q-learning can be used to find an optimal action-selection policy for any given
Quadratic classifierThis article is about machine learning and statistical classification. For other uses of the word
Quantum machine learningQuantum machine learning is a newly emerging interdisciplinary research area between quantum physics and computer science that summarises efforts to combine
Rademacher complexityIn computational learning theory (machine learning and theory of computation), Rademacher complexity, named after Hans Rademacher, measures richness of
Radford M. NealUniversity of Toronto, where he holds a Research Chair in statistics and machine learning. He studied computer science at the University of Calgary (B.Sc. 1977
Radial basis function kernelIn machine learning, the (Gaussian) radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms
Rage Against the MachineNot to be confused with Race Against the Machine. Rage Against the Machine (also known as RATM or simply Rage) is an American rock band from Los Angeles
Random forestThis article is about the machine learning technique. For other kinds of random tree, see Random tree (disambiguation). Random forests or random decision
Random subspace methodIn machine learning. the random subspace method, also called attribute bagging or feature bagging, is an ensemble learning method that attempts to reduce
Randomized weighted majority algorithmThe randomized weighted majority algorithm is an algorithm in machine learning theory. It improves the mistake bound of the weighted majority algorithm
Rayid Ghanihis graduate studies in the Machine Learning Department at Carnegie Mellon University with Tom M. Mitchell on Machine Learning and Text Classification and
Recurrent neural network(2002). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research 3:115-143. Jürgen Schmidhuber (2015). Deep learning in
RegularizationRegularization, in mathematics and statistics and particularly in the fields of machine learning and inverse problems, refers to a process of introducing additional
Regularization perspectives on support vector machinesperspectives on support vector machines provide a way of interpreting support vector machines (SVMs) in the context of other machine learning algorithms. SVM algorithms
Reinforcement learningreinforcement learning in psychology, see Reinforcement and Operant conditioning. Reinforcement learning is an area of machine learning inspired by behaviorist
Relevance vector machineIn mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression
Restricted Boltzmann machineBoltzmann machines, in particular the gradient-based contrastive divergence algorithm. Restricted Boltzmann machines can also be used in deep learning networks
Reza ZadehReza Zadeh is a Canadian Computer Scientist working on Machine Learning. He is faculty at Stanford University and serves on the technical advisory board
Richard S. SuttonPress, 1991. Sutton, R. S. (Ed.), Reinforcement Learning. Reprinting of a special issue of Machine Learning Journal. Kluwer Academic Press, 1992 Sutton
Richard ZemelDepartment of Computer Science, and a leading figure in the field of Machine Learning and Computer Vision. Zemel obtained his Ph.D under Geoffrey Hinton
Robert Schapiretheoretical and applied machine learning. His work led to the development of the boosting ensemble algorithm used in machine learning. Together with Yoav
Roberto BattitiSearch Optimization, which aims at embodying solvers with internal machine learning techniques, data mining and visualization. Battiti was elected Fellow
Robot learningRobot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills
Robustnessmany areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing
Ross D. KingManchester working at the Manchester Institute of Biotechnology and Machine Learning and Optimisation (MLO) group. King completed a Bachelor of Science
Ross Quinlanspecialist in artificial intelligence, particularly in the aspect involving machine learning and its application to data mining. Ross Quinlan invented the Iterative
Rote learningrote learning, at least for the more able students.[citation needed] Rote learning is also used to describe a simple learning pattern used in machine learning
Rule inductionRule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full
Rule-based machine learningRule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves
Russell Greinerin Machine Learning and Bioinformatics. Professor Greiner is one of the principal investigators at the Alberta Innovates Centre for Machine Learning (AICML)
Ryszard S. MichalskiProfessor at George Mason University and a pioneer in the field of machine learning. Michalski was born in Kalusz near Lvov on 7 May 1937. He received
SVMmodulating technique to give power to a load Support vector machine, in machine learning SVM (company) Saskatchewan Volunteer Medal Schuylkill Valley
Sample complexityThe sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function
Scikit-learnScikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification
Search spaceinput data in geometric search problems Version space, developed via machine learning, it is the subset of all hypotheses that are consistent with the observed
Self-learningSelf-learning can refer to: Learning Theory Autodidacticism unsupervised machine learning
Semantic analysisIn machine learning, semantic analysis of a corpus is the task of building structures that approximate concepts from a large set of documents. It generally
Semi-supervised learningsemi-supervised learning can be of great practical value. Semi-supervised learning is also of theoretical interest in machine learning and as a model for
Sepp HochreiterMühldorf) is a German computer scientist working in the fields of machine learning and bioinformatics. Since 2006 he has been head of the Institute of
Sequence labelingIn machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member
Shane LeggShane Legg is a machine learning researcher and founder of DeepMind Technologies, acquired by Google in 2014. Legg attended Rotorua Lakes High School
Shattered setthe study of empirical processes as well as in statistical computational learning theory. Suppose A is a set and C is a class of sets. The class C shatters
Shogunwritten in C++. It offers numerous algorithms and data structures for machine learning problems. Shogun is licensed under the terms of the GNU General Public
Similarity learningSimilarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the
Simple machinesoftware, see Simple Machines Forum. For a broader coverage related to this topic, see Mechanism (engineering). A simple machine is a mechanical device
Skymindsoftware packages focused on machine learning that can be installed and used together. SKIL is an operating system for deep learning, integrating with Hadoop
Skytreerestaurant, and observation tower in Sumida, Tokyo, Japan Skytree, Inc, a machine learning start up based in San Jose, California, USA Skytree DAC, a company
Skytree, Incdevelops machine learning software for enterprise use. Skytree came out of stealth mode in February 2012. announcing Skytree Server, a machine learning system
Solomonoff's theory of inductive inference 1023-1029. Burgin, M.; Klinger, A. Experience, Generations, and Limits in Machine Learning, Theoretical Computer Science, v. 317, No. 1/3, 2004, pp. 71-91 Davis
SpaCyspaCy also supports deep learning workflows that allow connecting statistical models trained by popular machine learning libraries like TensorFlow,
Sparse dictionary learningSparse dictionary learning is a representation learning method which aims at finding a sparse representation of the input data (also known as sparse coding)
Stabilityin computational learning theory of how a machine learning algorithm is perturbed by small changes to its inputs. A stable learning algorithm is one for
State-Action-Reward-State-Action(SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was introduced in a
Statistical classificationFor the unsupervised learning approach, see Cluster analysis. In machine learning and statistics, classification is the problem of identifying to which
Statistical learning theoryComputational learning theory This article is about statistical learning in machine learning. For its use in psychology, see Statistical learning in language
Statistical relational learningStatistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit
Stephen MuggletonFREng (born 6 December 1959, son of Louis Muggleton) is Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial
Steve OmohundroHamiltonian physics, dynamical systems, programming languages, machine learning, machine vision, and the social implications of artificial intelligence
Stochastic gradient descentM-estimation See also: Estimating equation Both statistical estimation and machine learning consider the problem of minimizing an objective function that has the
String kernelIn machine learning and data mining, a string kernel is a kernel function that operates on strings, i.e. finite sequences of symbols that need not be of
Structural risk minimizationminimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite
Structured predictionStructured prediction or structured (output) learning is an umbrella term for supervised machine learning techniques that involves predicting structured
Structured support vector machineThe structured support vector machine is a machine learning algorithm that generalizes the Support Vector Machine (SVM) classifier. Whereas the SVM classifier
Supervised learningSee also: Unsupervised learning Supervised learning is the machine learning task of inferring a function from labeled training data. The training data
Support vector machineVirtual Machine. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms
TanagraTanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon
Teaching machinetype of machine which used his ideas on how learning should be directed with positive reinforcement. Skinner advocated the use of teaching machines for a
Technology as a ServiceArtificial Intelligence (AI) () and Machine Learning to deliver IT services utilizing Digital Labor . Cognitive learning () and policy based governance intelligence
Temporal difference learningTemporal difference (TD) learning is a prediction-based machine learning method. It has primarily been used for the reinforcement learning problem, and is said
Tensor processing unitapplication-specific integrated circuits developed specifically for machine learning. Compared to graphics processing units (which as of 2016 are frequently
Test setrelationship. Test and training sets are used in intelligent systems, machine learning, genetic programming and statistics. Regression analysis was one
The Doomsday Machinedisrupting communications with Starfleet and Constellation. On learning of the approach of the machine, Decker pulls rank on First Officer Spock, assumes command
The Master AlgorithmThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote
The Time MachineThe Time Machine is a 2002 American science fiction film loosely adapted from the 1895 novel of the same name by H. G. Wells and the 1960 film screenplay
Theanoproject primarily developed by a machine learning group at the Universite de Montreal. Comparison of deep learning software Bergstra, J.; O. Breuleux;
Theoretical computer sciencecomplexity theory, distributed computation, parallel computation, VLSI, machine learning, computational biology, computational geometry, information theory
Thomas G. Dietterichfield of machine learning. He served as Executive Editor of Machine Learning (journal) (1992-98) and helped co-found the Journal of Machine Learning Research
Timeline of machine learningintelligence Machine learning Timeline of artificial intelligence Timeline of machine translation Marr, Marr.
Timeline of machine translationtimeline of machine translation. For a more detailed qualitative account, see the history of machine translation page. Timeline of machine learning Hutchins
Tom M. Mitchellthe Chair of the Machine Learning Department at CMU. Mitchell is known for his contributions to the advancement of machine learning, artificial intelligence
TorchTorch is an open source machine learning library, a scientific computing framework, and a script language based on the Lua programming language. It provides
Toyota Technological Institute at Chicagoprimarily on theoretical computer science (algorithms and complexity), machine learning (and related AI applications), programming languages (and related areas
Transductionsemi-supervised learning, since Vapnik's motivation is quite different. An example of an algorithm in this category is the Transductive Support Vector Machine (TSVM)
Trevor Hastieespecially in the field of machine learning, data mining, and bioinformatics. He has authored several popular books in statistical learning, including The Elements
Ulisses Braga NetoUniversity. His main research areas are statistical pattern recognition, machine learning, signal and image processing, and systems biology. He has worked extensively
United Learningincome Archived March 2, 2009, at the Wayback Machine. United Learning.
Universal portfolio algorithmportfolio algorithm is a portfolio selection algorithm from the field of machine learning and information theory. The algorithm learns adaptively from historical
Unsupervised learningUnsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples
Vanishing gradient problemIn machine learning, the vanishing gradient problem is a difficulty found in training artificial neural networks with gradient-based learning methods and
Vasant HonavarIndian born American computer scientist, and Artificial Intelligence, Machine Learning, Bioinformatics and Health Informatics researcher and educator. In
Vending machineWikibooks Learning resources from Wikiversity In Praise of Vending Machines - slideshow by Life magazine World's Strangest Vending Machines:, From Florida
Version space learningVersion space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined
Virtual scientific communityExamples of such communities include the Computational Intelligence and Machine Learning Portal or the Biomedical Informatics Research Network. There are numerous
Vladimir Vapnikthe Vapnik-Chervonenkis theory of statistical learning, and the co-inventor of the support vector machine method. Vladimir Vapnik was born in the Soviet
Vowpal Wabbitefficient scalable implementation of online machine learning and support for a number of machine learning reductions, importance weighting, and a selection
Wafflebulletin board service software program Waffles (machine learning), an open source collection of machine learning algorithms and tools Waffles (John Kerry),
WafflesWaffles is a collection of command-line tools for performing machine learning operations developed at Brigham Young University. These tools are written
Wake-sleep algorithmvariational Bayesian learning. After that, the algorithm was adapted to machine learning. It can be viewed as a way to train a Helmholtz Machine The wake-sleep
Walter DaelemansCenter (CLiPS). Daelemans pioneered the use of machine learning techniques, especially memory-based learning, in natural language processing in Europe in
War Machinearticle is about the superhero. For other uses, see War Machine (disambiguation). War Machine (James
WekaWaikato Environment for Knowledge Analysis (Weka) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New
WinnowThe winnow algorithm is a technique from machine learning for learning a linear classifier from labeled examples. It is very similar to the perceptron
Word embedding(2007).
Work outputengine's efficiency. NewPath Learning (1 March 2014). Work, Power and Simple Machines Science Learning Guide. NewPath Learning. pp.8. ISBN 978-1-63212-074-8
Xgboostof machine learning competitions. XGBoost initially started as a research project by Tianqi Chen as part of the Distributed (Deep) Machine Learning Community
Yann LeCunYann LeCun (born 1960) is a computer scientist with contributions in machine learning, computer vision, mobile robotics and computational neuroscience. He
Yaser Abu-Mostafaand Chairman of Machine Learning Consultants LLC. He is known for his research and educational activities in the area of machine learning. Abu-Mostafa
Yasuo MatsuyamaYasuo Matsuyama (born March 23, 1947) is a researcher in machine learning and human-aware information processing. He is a professor of Waseda University
Yoav Freundscience at the University of California, San Diego who mainly works on machine learning, probability theory and related fields and applications. He is best
Yoshua Bengiothe Learning in Machines and Brains project of the Canadian Institute for Advanced Research. Knight, Will (July 9, 2015).
ZerothAPI to interact with the platform. It makes a form of machine learning known as deep learning available to mobile devices. It is used for image and sound
Zhi-Hua ZhouLAMDA Group. His research interests include artificial intelligence, machine learning and data mining. Zhi-Hua Zhou received his B.Sc., M.Sc. and Ph.D
Zoubin GhahramaniGhahramani has made significant contributions in the areas of Bayesian machine learning (particularly variational methods for approximate Bayesian inference)