Outline of machine learning

{{Short description|Overview of and topical guide to machine learning}} {{Dynamic list|multiple=yes}}<!--... Attention: THIS IS AN OUTLINE

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'''Machine learning''' ('''ML''') is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory.<ref name=Britannica>http://www.britannica.com/EBchecked/topic/1116194/machine-learning {{tertiary source|date=February 2024}}</ref> In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed".<ref name="arthur_samuel_machine_learning_def">{{cite book | title=Too Big to Ignore: The Business Case for Big Data | publisher=Wiley | author=Phil Simon | date=March 18, 2013 | pages=89 | isbn=978-1-118-63817-0 | url=https://books.google.com/books?id=Dn-Gdoh66sgC&pg=PA89}}</ref> ML involves the study and construction of algorithms that can learn from and make predictions on data.<ref>{{cite journal |title=Glossary of terms |author1=Ron Kohavi |author2=Foster Provost |journal=Machine Learning |volume=30 |pages=271–274 |year=1998 |doi=10.1023/A:1007411609915 |url=https://ai.stanford.edu/~ronnyk/glossary.html|doi-access=free }}</ref> These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

== How can machine learning be categorized? ==

* An academic discipline * A branch of science ** An applied science *** A subfield of computer science **** A branch of artificial intelligence **** A subfield of soft computing **** Application of statistics

=== Paradigms of machine learning ===

* Supervised learning, where the model is trained on labeled data * Unsupervised learning, where the model tries to identify patterns in unlabeled data * Reinforcement learning, where the model learns to make decisions by receiving rewards or penalties.

== Applications of machine learning ==

* Applications of machine learning * Bioinformatics * Biomedical informatics * Computer vision * Customer relationship management * Data mining * Earth sciences * Email filtering * Inverted pendulum (balance and equilibrium system) * Natural language processing ** Named Entity Recognition ** Automatic summarization ** Automatic taxonomy construction ** Dialog system ** Grammar checker ** Language recognition *** Handwriting recognition *** Optical character recognition *** Speech recognition **** Text to Speech Synthesis **** Speech Emotion Recognition ** Machine translation ** Question answering ** Speech synthesis ** Text mining *** Term frequency–inverse document frequency ** Text simplification * Pattern recognition ** Facial recognition system ** Handwriting recognition ** Image recognition ** Optical character recognition ** Speech recognition * Recommendation system ** Collaborative filtering ** Content-based filtering ** Hybrid recommender systems * Search engine ** Search engine optimization * Social engineering

== Machine learning hardware ==

* Graphics processing unit * Tensor processing unit * Vision processing unit

== Machine learning tools == * Comparison of machine learning software * Comparison of deep learning software

=== Machine learning frameworks ===

==== Proprietary machine learning frameworks ====

* Amazon Machine Learning * Microsoft Azure Machine Learning Studio * DistBelief (replaced by TensorFlow)

==== Open source machine learning frameworks ====

* Apache Singa * Apache MXNet * Caffe * PyTorch * mlpack * TensorFlow * Torch * CNTK * Accord.Net * Jax * [https://github.com/alan-turing-institute/MLJ.jl MLJ.jl] – A machine learning framework for Julia

=== Machine learning libraries ===

* Deeplearning4j * Theano * scikit-learn * Keras

=== Machine learning algorithms === {{See also|Category:Machine learning algorithms|l1=List of machine learning algorithms}} {{div col|colwidth=22em}} * AdaBoost * Almeida–Pineda recurrent backpropagation * ALOPEX * Backpropagation * Bootstrap aggregating * CN2 algorithm * Constructing skill trees * Decision tree learning * Dehaene–Changeux model * Diffusion map * Dominance-based rough set approach * Dynamic time warping * Error-driven learning * Evolutionary multimodal optimization * Expectation–maximization algorithm * FastICA * Forward–backward algorithm * GeneRec * Genetic Algorithm for Rule Set Production * Growing self-organizing map * Hyper basis function network * IDistance * k-means clustering * ''k''-nearest neighbors algorithm * Kernel methods for vector output * Kernel principal component analysis * Learning vector quantization * Leabra * Linde–Buzo–Gray algorithm * Local outlier factor * Logic learning machine * LogitBoost * Manifold alignment * Markov chain Monte Carlo (MCMC) * Minimum redundancy feature selection * Mixture of experts * Multiple kernel learning * Naive Bayes classifier * Non-negative matrix factorization * Online machine learning * Out-of-bag error * Prefrontal cortex basal ganglia working memory * PVLV * Q-learning * Quadratic unconstrained binary optimization * Query-level feature * Quickprop * Radial basis function network * Random forest * Randomized weighted majority algorithm * Reinforcement learning * Repeated incremental pruning to produce error reduction (RIPPER) * Rprop * Rule-based machine learning * Self-organizing map * Skill chaining * Sparse PCA * State–action–reward–state–action * Stochastic gradient descent * Structured kNN * Support vector machine * T-distributed stochastic neighbor embedding * Temporal difference learning * Wake-sleep algorithm * Weighted majority algorithm (machine learning) {{div col end}}

== Machine learning methods ==

=== Instance-based algorithm === * K-nearest neighbors algorithm (KNN) * Learning vector quantization (LVQ) * Self-organizing map (SOM)

=== Regression analysis === * Logistic regression * Ordinary least squares regression (OLSR) * Linear regression * Stepwise regression * Multivariate adaptive regression splines (MARS) * Regularization algorithm ** Ridge regression ** Least Absolute Shrinkage and Selection Operator (LASSO) ** Elastic net ** Least-angle regression (LARS) * Classifiers ** Probabilistic classifier *** Naive Bayes classifier ** Binary classifier ** Linear classifier ** Hierarchical classifier

=== Dimensionality reduction ===

Dimensionality reduction * Canonical correlation analysis (CCA) * Factor analysis * Feature extraction * Feature selection * Independent component analysis (ICA) * Linear discriminant analysis (LDA) * Multidimensional scaling (MDS) * Non-negative matrix factorization (NMF) * Partial least squares regression (PLSR) * Principal component analysis (PCA) * Principal component regression (PCR) * Projection pursuit * Sammon mapping * t-distributed stochastic neighbor embedding (t-SNE)

=== Ensemble learning ===

Ensemble learning * AdaBoost * Boosting * Bootstrap aggregating (also "bagging" or "bootstrapping") * Ensemble averaging * Gradient boosted decision tree (GBDT) * Gradient boosting * Random Forest * Stacked Generalization

=== Meta-learning ===

Meta-learning * Inductive bias * Metadata

=== Reinforcement learning ===

Reinforcement learning * Q-learning * State–action–reward–state–action (SARSA) * Temporal difference learning (TD) * Learning Automata

=== Supervised learning ===

Supervised learning * Averaged one-dependence estimators (AODE) * Artificial neural network * Case-based reasoning * Gaussian process regression * Gene expression programming * Group method of data handling (GMDH) * Inductive logic programming * Instance-based learning * Lazy learning * Learning Automata * Learning Vector Quantization * Logistic Model Tree * Minimum message length (decision trees, decision graphs, etc.) ** Nearest Neighbor Algorithm ** Analogical modeling * Probably approximately correct learning (PAC) learning * Ripple down rules, a knowledge acquisition methodology * Symbolic machine learning algorithms * Support vector machines * Random Forests * Ensembles of classifiers ** Bootstrap aggregating (bagging) ** Boosting (meta-algorithm) * Ordinal classification * Conditional Random Field * ANOVA * Quadratic classifiers * k-nearest neighbor * Boosting ** SPRINT * Bayesian networks ** Naive Bayes * Hidden Markov models ** Hierarchical hidden Markov model

==== Bayesian ====

Bayesian statistics * Bayesian knowledge base * Naive Bayes * Gaussian Naive Bayes * Multinomial Naive Bayes * Averaged One-Dependence Estimators (AODE) * Bayesian Belief Network (BBN) * Bayesian Network (BN)

==== Decision tree algorithms ====

Decision tree algorithm * Decision tree * Classification and regression tree (CART) * Iterative Dichotomiser 3 (ID3) * C4.5 algorithm * C5.0 algorithm * Chi-squared Automatic Interaction Detection (CHAID) * Decision stump * Conditional decision tree * ID3 algorithm * Random forest * SLIQ

==== Linear classifier ====

Linear classifier * Fisher's linear discriminant * Linear regression * Logistic regression * Multinomial logistic regression * Naive Bayes classifier * Perceptron * Support vector machine

=== Unsupervised learning ===

Unsupervised learning * Expectation-maximization algorithm * Vector Quantization * Generative topographic map * Information bottleneck method * Association rule learning algorithms ** Apriori algorithm ** Eclat algorithm

==== Artificial neural networks ====

Artificial neural network * Feedforward neural network ** Extreme learning machine ** Convolutional neural network * Recurrent neural network ** Long short-term memory (LSTM) * Logic learning machine * Self-organizing map

==== Association rule learning ====

Association rule learning * Apriori algorithm * Eclat algorithm * FP-growth algorithm

==== Hierarchical clustering ====

Hierarchical clustering * Single-linkage clustering * Conceptual clustering

==== Cluster analysis ====

Cluster analysis * BIRCH * DBSCAN * Expectation–maximization (EM) * Fuzzy clustering * Hierarchical clustering * ''k''-means clustering * ''k''-medians * Mean-shift * OPTICS algorithm

==== Anomaly detection ====

Anomaly detection * ''k''-nearest neighbors algorithm (''k''-NN) * Local outlier factor

=== Semi-supervised learning ===

Semi-supervised learning * Active learning * Generative models * Low-density separation * Graph-based methods * Co-training * Transduction

=== Deep learning ===

Deep learning * Deep belief networks * Deep Boltzmann machines * Deep Convolutional neural networks * Deep Recurrent neural networks * Hierarchical temporal memory * Generative Adversarial Network ** Style transfer * Transformer * Stacked Auto-Encoders

=== Other machine learning methods and problems ===

* Anomaly detection * Association rules * Bias-variance dilemma * Classification ** Multi-label classification * Clustering * Data Pre-processing * Empirical risk minimization * Feature engineering * Feature learning * Learning to rank * Occam learning * Online machine learning * PAC learning * Regression * Reinforcement Learning * Semi-supervised learning * Statistical learning * Structured prediction ** Graphical models *** Bayesian network *** Conditional random field (CRF) *** Hidden Markov model (HMM) * Unsupervised learning * VC theory

== Machine learning research == * List of artificial intelligence projects * List of datasets for machine learning research

== History of machine learning ==

History of machine learning * Timeline of machine learning

== Machine learning projects ==

Machine learning projects: * DeepMind * Google Brain * OpenAI * Meta AI * Hugging Face

== Machine learning organizations ==

=== Machine learning conferences and workshops ===

* Artificial Intelligence and Security (AISec) (co-located workshop with CCS) * Conference on Neural Information Processing Systems (NIPS) * ECML PKDD * International Conference on Machine Learning (ICML) * [http://ml4all.org ML4ALL] (Machine Learning For All)

== Machine learning publications ==

=== Books on machine learning ===

* Mathematics for Machine Learning * Hands-On Machine Learning Scikit-Learn, Keras, and TensorFlow * The Hundred-Page Machine Learning Book

=== Machine learning journals ===

* ''Machine Learning'' * ''Journal of Machine Learning Research'' (JMLR) * ''Neural Computation''

== Persons influential in machine learning ==

* Alberto Broggi * Andrei Knyazev * Andrew McCallum * Andrew Ng * Anuraag Jain * Armin B. Cremers * Ayanna Howard * Barney Pell * Ben Goertzel * Ben Taskar * Bernhard Schölkopf * Brian D. Ripley * Christopher G. Atkeson * Corinna Cortes * Demis Hassabis * Douglas Lenat * Eric Xing * Ernst Dickmanns * Geoffrey Hinton * Hans-Peter Kriegel * Hartmut Neven * Heikki Mannila * Ian Goodfellow * Jacek M. Zurada * Jaime Carbonell * Jeremy Slovak * Jerome H. Friedman * John D. Lafferty * John Platt * Julie Beth Lovins * Jürgen Schmidhuber * Karl Steinbuch * Katia Sycara * Leo Breiman * Lise Getoor * Luca Maria Gambardella * Léon Bottou * Marcus Hutter * Mehryar Mohri * Michael Collins * Michael I. Jordan * Michael L. Littman * Nando de Freitas * Ofer Dekel * Oren Etzioni * Pedro Domingos * Peter Flach * Pierre Baldi * Pushmeet Kohli * Ray Kurzweil * Rayid Ghani * Ross Quinlan * Salvatore J. Stolfo * Sebastian Thrun * Selmer Bringsjord * Sepp Hochreiter * Shane Legg * Stephen Muggleton * Steve Omohundro * Tom M. Mitchell * Trevor Hastie * Vasant Honavar * Vladimir Vapnik * Yann LeCun * Yasuo Matsuyama * Yoshua Bengio * Zoubin Ghahramani

== See also ==

* Outline of artificial intelligence ** Outline of computer vision ** Outline of deep learning ** Outline of robotics  * Accuracy paradox * Action model learning * Activation function * Activity recognition * ADALINE * Adaptive neuro fuzzy inference system * Adaptive resonance theory * Additive smoothing * Adjusted mutual information * AIVA * AIXI * AlchemyAPI * AlexNet * Algorithm selection * Algorithmic inference * Algorithmic learning theory * AlphaGo * AlphaGo Zero * Alternating decision tree * Apprenticeship learning * Causal Markov condition  * Competitive learning * Concept learning * Decision tree learning * Differentiable programming * Distribution learning theory * Eager learning * End-to-end reinforcement learning * Error tolerance (PAC learning) * Explanation-based learning * Feature * GloVe * Hyperparameter * Inferential theory of learning * Learning automata * Learning classifier system * Learning rule * Learning with errors * M-Theory (learning framework) * Machine learning control * Machine learning in bioinformatics * Margin * Markov chain geostatistics  * Markov chain Monte Carlo (MCMC)  * Markov information source  * Markov logic network  * Markov model * Markov random field * Markovian discrimination  * Maximum-entropy Markov model  * Multi-armed bandit  * Multi-task learning * Multilinear subspace learning * Multimodal learning * Multiple instance learning * Multiple-instance learning * Never-Ending Language Learning * Offline learning * Parity learning * Population-based incremental learning * Predictive learning * Preference learning * Proactive learning * Proximal gradient methods for learning * Semantic analysis * Similarity learning * Sparse dictionary learning * Stability (learning theory) * Statistical learning theory * Statistical relational learning * Tanagra * Transfer learning * Variable-order Markov model  * Version space learning * Waffles * Weka * Loss function ** Loss functions for classification ** Mean squared error (MSE) ** Mean squared prediction error (MSPE) ** Taguchi loss function * Low-energy adaptive clustering hierarchy

=== Other ===

* Anne O'Tate * Ant colony optimization algorithms * Anthony Levandowski * Anti-unification (computer science) * Apache Flume * Apache Giraph * Apache Mahout * Apache SINGA * Apache Spark * Apache SystemML * Aphelion (software) * Arabic Speech Corpus * Archetypal analysis * Arthur Zimek * Artificial ants * Artificial bee colony algorithm * Artificial development * Artificial immune system * Astrostatistics * Averaged one-dependence estimators * Bag-of-words model * Balanced clustering * Ball tree * Base rate * Bat algorithm * Baum–Welch algorithm * Bayesian hierarchical modeling * Bayesian interpretation of kernel regularization * Bayesian optimization * Bayesian structural time series * Bees algorithm * Behavioral clustering * Bernoulli scheme * Bias–variance tradeoff * Biclustering * BigML * Binary classification * Bing Predicts * Bio-inspired computing * Biogeography-based optimization * Biplot * Bondy's theorem * Bongard problem * Bradley–Terry model * BrownBoost * Brown clustering * Burst error * CBCL (MIT) * CIML community portal * CMA-ES * CURE data clustering algorithm * Cache language model * Calibration (statistics) * Canonical correspondence analysis * Canopy clustering algorithm * Cascading classifiers * Category utility * CellCognition * Cellular evolutionary algorithm * Chi-square automatic interaction detection * Chromosome (genetic algorithm) * Classifier chains * Cleverbot * Clonal selection algorithm * Cluster-weighted modeling * Clustering high-dimensional data * Clustering illusion * CoBoosting * Cobweb (clustering) * Cognitive computer * Cognitive robotics * Collostructional analysis * Common-method variance * Complete-linkage clustering * Computer-automated design * Concept class * Concept drift * Conference on Artificial General Intelligence * Conference on Knowledge Discovery and Data Mining * Confirmatory factor analysis * Confusion matrix * Congruence coefficient * Connect (computer system) * Consensus clustering * Constrained clustering * Constrained conditional model * Constructive cooperative coevolution * Correlation clustering * Correspondence analysis * Cortica * Coupled pattern learner * Cross-entropy method * Cross-validation (statistics) * Crossover (genetic algorithm) * Cuckoo search * Cultural algorithm * Cultural consensus theory * Curse of dimensionality * DADiSP * DARPA LAGR Program * Darkforest * Dartmouth workshop * DarwinTunes * Data Mining Extensions * Data exploration * Data pre-processing * Data stream clustering * Dataiku * Davies–Bouldin index * Decision boundary * Decision list * Decision tree model * Deductive classifier * DeepArt * DeepDream * Deep Web Technologies * Defining length * Dendrogram * Dependability state model * Detailed balance * Determining the number of clusters in a data set * Detrended correspondence analysis * Developmental robotics * Diffbot * Differential evolution * Discrete phase-type distribution * Discriminative model * Dissociated press * Distributed R * Dlib * Document classification * Documenting Hate * Domain adaptation * Doubly stochastic model * Dual-phase evolution * Dunn index * Dynamic Bayesian network * Dynamic Markov compression * Dynamic topic model * Dynamic unobserved effects model * EDLUT * ELKI * Edge recombination operator * Effective fitness * Elastic map * Elastic matching * Elbow method (clustering) * Emergent (software) * Encog * Entropy rate * Erkki Oja * Eurisko * European Conference on Artificial Intelligence * Evaluation of binary classifiers * Evolution strategy * Evolution window * Evolutionary Algorithm for Landmark Detection * Evolutionary algorithm * Evolutionary art * Evolutionary music * Evolutionary programming * Evolvability (computer science) * Evolved antenna * Evolver (software) * Evolving classification function * Expectation propagation * Exploratory factor analysis * F1 score * FLAME clustering * Factor analysis of mixed data * Factor graph * Factor regression model * Factored language model * Farthest-first traversal * Fast-and-frugal trees * Feature Selection Toolbox * Feature hashing * Feature scaling * Feature vector * Firefly algorithm * First-difference estimator * First-order inductive learner * Fish School Search * Fisher kernel * Fitness approximation * Fitness function * Fitness proportionate selection * Fluentd * Folding@home * Formal concept analysis * Forward algorithm * Fowlkes–Mallows index * Frederick Jelinek * Frrole * Functional principal component analysis * GATTO * GLIMMER * Gary Bryce Fogel * Gaussian adaptation * Gaussian process * Gaussian process emulator * Gene prediction * General Architecture for Text Engineering * Generalization error * Generalized canonical correlation * Generalized filtering * Generalized iterative scaling * Generalized multidimensional scaling * Generative adversarial network * Generative model * Genetic algorithm * Genetic algorithm scheduling * Genetic algorithms in economics * Genetic fuzzy systems * Genetic memory (computer science) * Genetic operator * Genetic programming * Genetic representation * Geographical cluster * Gesture Description Language * Geworkbench * Glossary of artificial intelligence * Glottochronology * Golem (ILP) * Google matrix * Grafting (decision trees) * Gramian matrix * Grammatical evolution * Granular computing * GraphLab * Graph kernel * Gremlin (programming language) * Growth function * HUMANT (HUManoid ANT) algorithm * Hammersley–Clifford theorem * Harmony search * Hebbian theory * Hidden Markov random field * Hidden semi-Markov model * Hierarchical hidden Markov model * Higher-order factor analysis * Highway network * Hinge loss * Holland's schema theorem * Hopkins statistic * Hoshen–Kopelman algorithm * Huber loss * IRCF360 * Ian Goodfellow * Ilastik * Ilya Sutskever * Immunocomputing * Imperialist competitive algorithm * Inauthentic text * Incremental decision tree * Induction of regular languages * Inductive bias * Inductive probability * Inductive programming * Influence diagram * Information Harvesting * Information gain in decision trees * Information gain ratio * Inheritance (genetic algorithm) * Instance selection * Intel RealSense * Interacting particle system * Interactive machine translation * International Joint Conference on Artificial Intelligence * International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics * International Semantic Web Conference * Iris flower data set * Island algorithm * Isotropic position * Item response theory * Iterative Viterbi decoding * JOONE * Jabberwacky * Jaccard index * Jackknife variance estimates for random forest * Java Grammatical Evolution * Joseph Nechvatal * Jubatus * Julia (programming language) * Junction tree algorithm * ''k''-SVD * ''k''-means++ * ''k''-medians clustering * ''k''-medoids * KNIME * KXEN Inc. * ''k q''-flats * Kaggle * Kalman filter * Katz's back-off model * Kernel adaptive filter * Kernel density estimation * Kernel eigenvoice * Kernel embedding of distributions * Kernel method * Kernel perceptron * Kernel random forest * Kinect * Klaus-Robert Müller * Kneser–Ney smoothing * Knowledge Vault * Knowledge integration * LIBSVM * LPBoost * Labeled data * LanguageWare * Language identification in the limit * Language model * Large margin nearest neighbor * Latent Dirichlet allocation * Latent class model * Latent semantic analysis * Latent variable * Latent variable model * Lattice Miner * Layered hidden Markov model * Learnable function class * Least squares support vector machine * Leslie P. Kaelbling * Linear genetic programming * Linear predictor function * Linear separability * Linkurious * Lior Ron (business executive) * List of genetic algorithm applications * List of metaphor-based metaheuristics * List of text mining software * Local case-control sampling * Local independence * Local tangent space alignment * Locality-sensitive hashing * Log-linear model * Logistic model tree * Low-rank approximation * Low-rank matrix approximations * MATLAB * MIMIC (immunology) * MXNet * Mallet (software project) * Manifold regularization * Margin-infused relaxed algorithm * Margin classifier * Mark V. Shaney * Massive Online Analysis * Matrix regularization * Matthews correlation coefficient * Mean shift * Mean squared error * Mean squared prediction error * Measurement invariance * Medoid * MeeMix * Melomics * Memetic algorithm * Meta-optimization * Mexican International Conference on Artificial Intelligence * Michael Kearns (computer scientist) * MinHash * Mixture model * Mlpy * Models of DNA evolution * Moral graph * Mountain car problem * Movidius * Multi-armed bandit * Multi-label classification * Multi expression programming * Multiclass classification * Multidimensional analysis * Multifactor dimensionality reduction * Multilinear principal component analysis * Multiple correspondence analysis * Multiple discriminant analysis * Multiple factor analysis * Multiple sequence alignment * Multiplicative weight update method * Multispectral pattern recognition * Mutation (genetic algorithm) * N-gram * NOMINATE (scaling method) * Native-language identification * Natural Language Toolkit * Natural evolution strategy * Nearest-neighbor chain algorithm * Nearest centroid classifier * Nearest neighbor search * Neighbor joining * Nest Labs * NetMiner * NetOwl * Neural Designer * Neural Engineering Object * Neural modeling fields * Neural network software * NeuroSolutions * Neuroevolution * Neuroph * Niki.ai * Noisy channel model * Noisy text analytics * Nonlinear dimensionality reduction * Novelty detection * Nuisance variable * One-class classification * Onnx * OpenNLP * Optimal discriminant analysis * Oracle Data Mining * Orange (software) * Ordination (statistics) * Overfitting * PROGOL * PSIPRED * Pachinko allocation * PageRank * Parallel metaheuristic * Parity benchmark * Part-of-speech tagging * Particle swarm optimization * Path dependence * Pattern language (formal languages) * Peltarion Synapse * Perplexity * Persian Speech Corpus * Pietro Perona * Pipeline Pilot * Piranha (software) * Pitman–Yor process * Plate notation * Polynomial kernel * Pop music automation * Population process * Portable Format for Analytics * Predictive Model Markup Language * Predictive state representation * Preference regression * Premature convergence * Principal geodesic analysis * Prior knowledge for pattern recognition * Prisma (app) * Probabilistic Action Cores * Probabilistic context-free grammar * Probabilistic latent semantic analysis * Probabilistic soft logic * Probability matching * Probit model * Product of experts * Programming with Big Data in R * Proper generalized decomposition * Pruning (decision trees) * Pushpak Bhattacharyya * Q methodology * Qloo * Quality control and genetic algorithms * Quantum Artificial Intelligence Lab * Queueing theory * Quick, Draw! * R (programming language) * Rada Mihalcea * Rademacher complexity * Radial basis function kernel * Rand index * Random indexing * Random projection * Random subspace method * Ranking SVM * RapidMiner * Rattle GUI * Raymond Cattell * Reasoning system * Regularization perspectives on support vector machines * Relational data mining * Relationship square * Relevance vector machine * Relief (feature selection) * Renjin * Repertory grid * Representer theorem * Reward-based selection * Richard Zemel * Right to explanation * RoboEarth * Robust principal component analysis * RuleML Symposium * Rule induction * Rules extraction system family * SAS (software) * SNNS * SPSS Modeler * SUBCLU * Sample complexity * Sample exclusion dimension * Santa Fe Trail problem * Savi Technology * Schema (genetic algorithms) * Search-based software engineering * Selection (genetic algorithm) * Self-Service Semantic Suite * Semantic folding * Semantic mapping (statistics) * Semidefinite embedding * Sense Networks * Sensorium Project * Sequence labeling * Sequential minimal optimization * Shattered set * Shogun (toolbox) * Silhouette (clustering) * SimHash * SimRank * Similarity measure * Simple matching coefficient * Simultaneous localization and mapping * Sinkov statistic * Sliced inverse regression * Snakes and Ladders * Soft independent modelling of class analogies * Soft output Viterbi algorithm * Solomonoff's theory of inductive inference * SolveIT Software * Spectral clustering * Spike-and-slab variable selection * Statistical machine translation * Statistical parsing * Statistical semantics * Stefano Soatto * Stephen Wolfram * Stochastic block model * Stochastic cellular automaton * Stochastic diffusion search * Stochastic grammar * Stochastic matrix * Stochastic universal sampling * Stress majorization * String kernel * Structural equation modeling * Structural risk minimization * Structured sparsity regularization * Structured support vector machine * Subclass reachability * Sufficient dimension reduction * Sukhotin's algorithm * Sum of absolute differences * Sum of absolute transformed differences * Swarm intelligence * Switching Kalman filter * Symbolic regression * Synchronous context-free grammar * Syntactic pattern recognition * TD-Gammon * TIMIT * Teaching dimension * Teuvo Kohonen * Textual case-based reasoning * Theory of conjoint measurement * Thomas G. Dietterich * Thurstonian model * Topic model * Tournament selection * Training, test, and validation sets * Transiogram * Trax Image Recognition * Trigram tagger * Truncation selection * Tucker decomposition * UIMA * UPGMA * Ugly duckling theorem * Uncertain data * Uniform convergence in probability * Unique negative dimension * Universal portfolio algorithm * User behavior analytics * VC dimension * VIGRA * Validation set * Vapnik–Chervonenkis theory * Variable-order Bayesian network * Variable kernel density estimation * Variable rules analysis * Variational message passing * Varimax rotation * Vector quantization * Vicarious (company) * Viterbi algorithm * Vowpal Wabbit * WACA clustering algorithm * WPGMA * Ward's method * Weasel program * Whitening transformation * Winnow (algorithm) * Win–stay, lose–switch * Witness set * Wolfram Language * Wolfram Mathematica * Writer invariant * Xgboost * Yooreeka * Zeroth (software)

== Further reading ==

* Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). ''[https://web.archive.org/web/20091110212529/http://www-stat.stanford.edu/~tibs/ElemStatLearn/ The Elements of Statistical Learning]'', Springer. {{ISBN|0-387-95284-5}}. * Pedro Domingos (September 2015), The Master Algorithm, Basic Books, {{ISBN|978-0-465-06570-7}} * Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). ''[http://www.cs.nyu.edu/~mohri/mlbook/ Foundations of Machine Learning]'', The MIT Press. {{ISBN|978-0-262-01825-8}}. * Ian H. Witten and Eibe Frank (2011). ''Data Mining: Practical machine learning tools and techniques'' Morgan Kaufmann, 664pp., {{ISBN|978-0-12-374856-0}}. * David J. C. MacKay. ''[http://www.inference.phy.cam.ac.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms]'' Cambridge: Cambridge University Press, 2003. {{ISBN|0-521-64298-1}} * Richard O. Duda, Peter E. Hart, David G. Stork (2001) ''Pattern classification'' (2nd edition), Wiley, New York, {{ISBN|0-471-05669-3}}. * Christopher Bishop (1995). ''Neural Networks for Pattern Recognition'', Oxford University Press. {{ISBN|0-19-853864-2}}. * Vladimir Vapnik (1998). ''Statistical Learning Theory''. Wiley-Interscience, {{ISBN|0-471-03003-1}}. * Ray Solomonoff, ''An Inductive Inference Machine'', IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957. * Ray Solomonoff, "[http://world.std.com/~rjs/indinf56.pdf An Inductive Inference Machine]" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.

== References == {{Reflist}}

== External links == {{Sister project links|Machine learning}}

* [https://web.archive.org/web/20170118041300/https://mitprofessionalx.mit.edu/courses/course-v1:MITProfessionalX+DSx+2016_T1/about Data Science: Data to Insights from MIT (machine learning)] * Popular online course by Andrew Ng, at [https://www.coursera.org/course/ml Coursera]. It uses GNU Octave. The course is a free version of Stanford University's actual course taught by Ng, see.stanford.edu/Course/CS229 available for free]. * [https://mloss.org/ mloss] is an academic database of open-source machine learning software.

Machine learning Machine learning Category:Computing-related lists * Machine learning