• Source: 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




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 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
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


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






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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