Outline of machine learning

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. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". ML involves the study and construction of algorithms that can learn from and make predictions on data. 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 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 MLJ.jl – A machine learning framework for Julia

=== Machine learning libraries === Deeplearning4j Theano scikit-learn Keras

=== Machine learning algorithms === 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 ==

=== 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) 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 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 dat...

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Machine Learning - Data Science