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Outline of machine learning

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The following outline is provided as an overview of, and topical guide to, machine learning:

Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory.cite-ref-britannica-1-0[1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed".cite-ref-arthur-samuel-machine-learning-def-2-0[2] ML involves the study and construction of algorithms that can learn from and make predictions on data.cite-ref-3[3] 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.

Contents

• How can machine learning be categorized?

• Paradigms of machine learning

• Applications of machine learning

• Machine learning hardware

• Machine learning tools

• Machine learning frameworks

• Machine learning libraries

• Machine learning algorithms

• Machine learning methods

• Instance-based algorithm

• Regression analysis

• Dimensionality reduction

• Ensemble learning

• Meta-learning

• Reinforcement learning

• Supervised learning

• Unsupervised learning

• Semi-supervised learning

• Deep learning

• Other machine learning methods and problems

• Machine learning research

• History of machine learning

• Machine learning projects

• Machine learning organizations

• Machine learning conferences and workshops

• Machine learning publications

• Books on machine learning

• Machine learning journals

• Persons influential in machine learning

• Other

• Further reading

• External links

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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 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

• CNTK

• Jax

• MLJ.jl – A machine learning framework for Julia

Machine learning libraries

• Deeplearning4j

• scikit-learn

Machine learning algorithms

• Almeida–Pineda recurrent backpropagation

• Backpropagation

• Bootstrap aggregating

• CN2 algorithm

• Constructing skill trees

• Dehaene–Changeux model

• Diffusion map

• Dominance-based rough set approach

• Dynamic time warping

• Error-driven learning

• Evolutionary multimodal optimization

• Expectation–maximization algorithm

• Forward–backward algorithm

• Genetic Algorithm for Rule Set Production

• Growing self-organizing map

• Hyper basis function network

• k-nearest neighbors algorithm

• Kernel methods for vector output

• Kernel principal component analysis

• Linde–Buzo–Gray algorithm

• Local outlier factor

• Logic learning machine

• Manifold alignment

• Markov chain Monte Carlo (MCMC)

• Minimum redundancy feature selection

• Mixture of experts

• Multiple kernel learning

• Non-negative matrix factorization

• Online machine learning

• Out-of-bag error

• Prefrontal cortex basal ganglia working memory

• PVLV

• Quadratic unconstrained binary optimization

• Query-level feature

• Radial basis function network

• Randomized weighted majority algorithm

• Reinforcement learning

• Repeated incremental pruning to produce error reduction (RIPPER)

• Rule-based machine learning

• Skill chaining

• State–action–reward–state–action

• Stochastic gradient descent

• Structured kNN

• T-distributed stochastic neighbor embedding

• Temporal difference learning

• Wake-sleep algorithm

• Weighted majority algorithm (machine learning)

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

• Bootstrap aggregating (also "bagging" or "bootstrapping")

• Ensemble averaging

• Gradient boosted decision tree (GBDT)

• Gradient boosting

• Random Forest

• Stacked Generalization

Meta-learning

• Inductive bias

Reinforcement learning

Reinforcement 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

• Quadratic classifiers

• k-nearest neighbor

• 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

• 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

• Expectation–maximization (EM)

• Fuzzy clustering

• Hierarchical clustering

• k-means clustering

• 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 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

• Data Pre-processing

• Empirical risk minimization

• Feature engineering

• Feature learning

• Learning to rank

• Occam learning

• Online machine learning

• PAC learning

• Reinforcement Learning

• Semi-supervised learning

• Statistical learning

• Structured prediction

• Graphical models

• Bayesian network

• Conditional random field (CRF)

• Hidden Markov model (HMM)

• Unsupervised learning

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:

• Google Brain

• 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)

• International Conference on Machine Learning (ICML)

• 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

• Anuraag Jain

• Armin B. Cremers

• Ayanna Howard

• Barney Pell

• Ben Goertzel

• Bernhard Schölkopf

• Brian D. Ripley

• Christopher G. Atkeson

• Corinna Cortes

• Demis Hassabis

• Douglas Lenat

• 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

• 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

• 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

• Stephen Muggleton

• Steve Omohundro

• Tom M. Mitchell

• Trevor Hastie

• Vasant Honavar

• Vladimir Vapnik

• Yasuo Matsuyama

• Yoshua Bengio

• Zoubin Ghahramani

See also

• Outline of artificial intelligence

• Outline of computer vision

• Outline of robotics

• Accuracy paradox

• Action model learning

• Activation function

• Activity recognition

• Adaptive neuro fuzzy inference system

• Adaptive resonance theory

• Additive smoothing

• Adjusted mutual information

• AIVA

• AIXI

• Algorithm selection

• Algorithmic inference

• Algorithmic learning theory

• 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

• 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

• 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

• Transfer learning

• Variable-order Markov model

• Version space learning

• 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

• 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

• Bondy's theorem

• Bongard problem

• Bradley–Terry model

• Brown clustering

• Burst error

• CIML community portal

• 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

• Clonal selection algorithm

• Cluster-weighted modeling

• Clustering high-dimensional data

• Clustering illusion

• 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

• Coupled pattern learner

• Cross-entropy method

• Cross-validation (statistics)

• Crossover (genetic algorithm)

• Cuckoo search

• Cultural algorithm

• Cultural consensus theory

• Curse of dimensionality

• DARPA LAGR Program

• Dartmouth workshop

• DarwinTunes

• Data Mining Extensions

• Data exploration

• Data pre-processing

• Data stream clustering

• Davies–Bouldin index

• Decision boundary

• Decision list

• Decision tree model

• Deductive classifier

• Deep Web Technologies

• Defining length

• Dependability state model

• Detailed balance

• Determining the number of clusters in a data set

• Detrended correspondence analysis

• Developmental robotics

• 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

• Dynamic Bayesian network

• Dynamic Markov compression

• Dynamic topic model

• Dynamic unobserved effects model

• ELKI

• Edge recombination operator

• Effective fitness

• Elastic map

• Elastic matching

• Elbow method (clustering)

• Emergent (software)

• Entropy rate

• 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

• 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

• Folding@home

• Formal concept analysis

• Forward algorithm

• Fowlkes–Mallows index

• Frederick Jelinek

• Functional principal component analysis

• 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

• 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

• Holland's schema theorem

• Hopkins statistic

• Hoshen–Kopelman algorithm

• Ian Goodfellow

• 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

• Jabberwacky

• Jaccard index

• Jackknife variance estimates for random forest

• Java Grammatical Evolution

• Joseph Nechvatal

• Julia (programming language)

• Junction tree algorithm

• k-medians clustering

• 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

• Klaus-Robert Müller

• Kneser–Ney smoothing

• Knowledge Vault

• Knowledge integration

• 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

• 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

• MIMIC (immunology)

• Mallet (software project)

• Manifold regularization

• Margin-infused relaxed algorithm

• Margin classifier

• Mark V. Shaney

• Massive Online Analysis

• Matrix regularization

• Matthews correlation coefficient

• Mean squared error

• Mean squared prediction error

• Measurement invariance

• Memetic algorithm

• Meta-optimization

• Mexican International Conference on Artificial Intelligence

• Michael Kearns (computer scientist)

• Mixture model

• Mlpy

• Models of DNA evolution

• Moral graph

• Mountain car problem

• 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)

• NOMINATE (scaling method)

• Native-language identification

• Natural Language Toolkit

• Natural evolution strategy

• Nearest-neighbor chain algorithm

• Nearest centroid classifier

• Nearest neighbor search

• Neighbor joining

• Neural Designer

• Neural Engineering Object

• Neural modeling fields

• Neural network software

• NeuroSolutions

• Neuroevolution

• Noisy channel model

• Noisy text analytics

• Nonlinear dimensionality reduction

• Novelty detection

• Nuisance variable

• One-class classification

• Onnx

• Optimal discriminant analysis

• Oracle Data Mining

• Orange (software)

• Ordination (statistics)

• Overfitting

• Pachinko allocation

• Parallel metaheuristic

• Parity benchmark

• Part-of-speech tagging

• Particle swarm optimization

• Path dependence

• Pattern language (formal languages)

• Peltarion Synapse

• 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

• Random indexing

• Random projection

• Random subspace method

• Ranking SVM

• Raymond Cattell

• Reasoning system

• Regularization perspectives on support vector machines

• Relational data mining

• Relationship square

• Relevance vector machine

• Relief (feature selection)

• Repertory grid

• Representer theorem

• Reward-based selection

• Richard Zemel

• Right to explanation

• Robust principal component analysis

• RuleML Symposium

• Rule induction

• Rules extraction system family

• SAS (software)

• SNNS

• SPSS Modeler

• 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)

• 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

• 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

• Ugly duckling theorem

• Uncertain data

• Uniform convergence in probability

• Unique negative dimension

• Universal portfolio algorithm

• User behavior analytics

• VC dimension

• 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

• Ward's method

• Weasel program

• Whitening transformation

• Winnow (algorithm)

• Win–stay, lose–switch

• Witness set

• Wolfram Language

• Wolfram Mathematica

• Writer invariant

• Zeroth (software)

Further reading

• Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). 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). 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. 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, "An Inductive Inference Machine" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.

References

cite-note-britannica-11. ↑ http://www.britannica.com/EBchecked/topic/1116194/machine-learning This tertiary source reuses information from other sources but does not name them.

cite-note-arthur-samuel-machine-learning-def-22. ↑ citerefphil-simon2013Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN 978-1-118-63817-0.

cite-note-33. ↑ citerefron-kohavifoster-provost1998Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915.

External links

• Data Science: Data to Insights from MIT (machine learning)

• Popular online course by Andrew Ng, at 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].

• mloss is an academic database of open-source machine learning software.

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