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

Machine learning – subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.^[1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed".^[2] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.^[3] Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

What type of thing is machine learning?[edit]

• 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

Branches of machine learning[edit]

Subfields of machine learning[edit]

• Computational learning theory – studying the design and analysis of machine learning algorithms.^[4]
• Grammar induction
• Meta-learning

Cross-disciplinary fields involving machine learning[edit]

• Adversarial machine learning
• Predictive analytics
• Quantum machine learning
• Robot learning
  • Developmental robotics

Applications of machine learning[edit]

• 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 (NLP)
  • 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 (TTS)
      • Speech Emotion Recognition (SER)
  • Machine translation
  • Question answering
  • Speech synthesis
  • Text mining
    • Term frequency–inverse document frequency (tf–idf)
  • 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 (Collaborative and content-based filtering)
• Search engine
  • Search engine optimization
• Social Engineering

Machine learning hardware[edit]

• Graphics processing unit
• Tensor processing unit
• Vision processing unit

Machine learning tools[edit]

• Comparison of deep learning software

Machine learning frameworks[edit]

Proprietary machine learning frameworks[edit]

• Amazon Machine Learning
• Microsoft Azure Machine Learning Studio
• DistBelief – replaced by TensorFlow

Open source machine learning frameworks[edit]

• Apache Singa
• Apache MXNet
• Caffe
• PyTorch
• mlpack
• TensorFlow
• Torch
• CNTK
• Accord.Net
• Jax
• MLJ.jl – A machine learning framework for Julia

Machine learning libraries[edit]

• Deeplearning4j
• Theano
• scikit-learn
• Keras

Machine learning algorithms[edit]

• Almeida–Pineda recurrent backpropagation
• ALOPEX
• 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
• FastICA
• Forward–backward algorithm
• GeneRec
• Genetic Algorithm for Rule Set Production
• Growing self-organizing map
• Hyper basis function network
• IDistance
• K-nearest neighbors algorithm
• Kernel methods for vector output
• Kernel principal component analysis
• 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
• 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
• Randomized weighted majority algorithm
• Reinforcement learning
• Repeated incremental pruning to produce error reduction (RIPPER)
• Rprop
• Rule-based machine learning
• Skill chaining
• Sparse PCA
• 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[edit]

Instance-based algorithm[edit]

• K-nearest neighbors algorithm (KNN)
• Learning vector quantization (LVQ)
• Self-organizing map (SOM)

Regression analysis[edit]

• 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[edit]

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[edit]

Ensemble learning

• AdaBoost
• Boosting
• Bootstrap aggregating (Bagging)
• Ensemble averaging – process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out."
• Gradient boosted decision tree (GBDT)
• Gradient boosting machine (GBM)
• Random Forest
• Stacked Generalization (blending)

Meta-learning[edit]

Meta-learning

• Inductive bias
• Metadata

Reinforcement learning[edit]

Reinforcement learning

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

Supervised learning[edit]

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[edit]

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[edit]

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[edit]

Linear classifier

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

Unsupervised learning[edit]

Unsupervised learning

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

Artificial neural networks[edit]

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[edit]

Association rule learning

• Apriori algorithm
• Eclat algorithm
• FP-growth algorithm

Hierarchical clustering[edit]

Hierarchical clustering

• Single-linkage clustering
• Conceptual clustering

Cluster analysis[edit]

Cluster analysis

• BIRCH
• DBSCAN
• Expectation-maximization (EM)
• Fuzzy clustering
• Hierarchical Clustering
• K-means clustering
• K-medians
• Mean-shift
• OPTICS algorithm

Anomaly detection[edit]

Anomaly detection

• k-nearest neighbors algorithm (k-NN)
• Local outlier factor

Semi-supervised learning[edit]

Semi-supervised learning

• Active learning – special case of semi-supervised learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points.^[5][6]
• Generative models
• Low-density separation
• Graph-based methods
• Co-training
• Transduction

Deep learning[edit]

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[edit]

• 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[edit]

• List of artificial intelligence projects
• List of datasets for machine learning research

History of machine learning[edit]

History of machine learning

• Timeline of machine learning

Machine learning projects[edit]

Machine learning projects

• DeepMind
• Google Brain
• OpenAI
• Meta AI

Machine learning organizations[edit]

Machine learning organizations

Machine learning conferences and workshops[edit]

• 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)
• ML4ALL (Machine Learning For All)

Machine learning publications[edit]

Books on machine learning[edit]

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

Machine learning journals[edit]

• Machine Learning
• Journal of Machine Learning Research (JMLR)
• Neural Computation

Persons influential in machine learning[edit]

• 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 – co-inventor of the backpropagation and contrastive divergence training algorithms
• Hans-Peter Kriegel
• Hartmut Neven
• Heikki Mannila
• Ian Goodfellow – Father of Generative & adversarial networks
• Jacek M. Zurada
• Jaime Carbonell
• Jeremy Slovak
• Jerome H. Friedman
• John D. Lafferty
• John Platt – invented SMO and Platt scaling
• Julie Beth Lovins
• Jürgen Schmidhuber
• Karl Steinbuch
• Katia Sycara
• Leo Breiman – invented bagging and random forests
• 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 – co-inventor of the SVM and VC theory
• Yann LeCun – invented convolutional neural networks
• Yasuo Matsuyama
• Yoshua Bengio
• Zoubin Ghahramani

See also[edit]

• Outline of artificial intelligence
  • Outline of computer vision
• 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[edit]

• 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
• Leave-one-out error
• Leslie P. Kaelbling
• Linear genetic programming
• Linear predictor function
• Linear separability
• Lingyun Gu
• 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)
• MysteryVibe
• 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
• Picas (app)
• 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[edit]

• 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[edit]

External links[edit]

• 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.