Outline of machine learning
{{Short description|Overview of and topical guide to 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.http://www.britannica.com/EBchecked/topic/1116194/machine-learning {{tertiary source|date=February 2024}} In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed".{{cite book | title=Too Big to Ignore: The Business Case for Big Data | publisher=Wiley | author=Phil Simon | date=March 18, 2013 | pages=89 | isbn=978-1-118-63817-0 | url=https://books.google.com/books?id=Dn-Gdoh66sgC&pg=PA89}} ML involves the study and construction of algorithms that can learn from and make predictions on data.{{cite journal |title=Glossary of terms |author1=Ron Kohavi |author2=Foster Provost |journal=Machine Learning |volume=30 |pages=271–274 |year=1998 |doi=10.1023/A:1007411609915 |url=https://ai.stanford.edu/~ronnyk/glossary.html|doi-access=free }} 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.
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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
Machine learning tools
= Machine learning frameworks =
== Proprietary machine learning frameworks ==
== Open source machine learning frameworks ==
- Apache Singa
- Apache MXNet
- Caffe
- PyTorch
- mlpack
- TensorFlow
- Torch
- CNTK
- Accord.Net
- Jax
- [https://github.com/alan-turing-institute/MLJ.jl MLJ.jl] – A machine learning framework for Julia
= Machine learning libraries =
= 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 =
- 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 =
- AdaBoost
- Boosting
- Bootstrap aggregating (also "bagging" or "bootstrapping")
- Ensemble averaging
- Gradient boosted decision tree (GBDT)
- Gradient boosting
- Random Forest
- Stacked Generalization
= Meta-learning =
= Reinforcement 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 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 ==
= Unsupervised learning =
== Artificial neural networks ==
== Association rule learning ==
== Hierarchical clustering ==
== Cluster analysis ==
== Anomaly detection ==
= Semi-supervised learning =
= Deep learning =
= 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
History of machine learning
Machine learning projects
Machine learning projects:
Machine learning organizations
= Machine learning conferences and workshops =
- Artificial Intelligence and Security (AISec) (co-located workshop with CCS)
- Conference on Neural Information Processing Systems (NIPS)
- ECML PKDD
- International Conference on Machine Learning (ICML)
- [http://ml4all.org ML4ALL] (Machine Learning For All)
Machine learning publications
= Books on machine learning =
= Machine learning journals =
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
- 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
- Pietro Perona
- Pipeline Pilot
- Piranha (software)
- Pitman–Yor process
- Plate notation
- Polynomial kernel
- Pop music automation
- Population process
- Portable Format for Analytics
- Predictive Model Markup Language
- Predictive state representation
- Preference regression
- Premature convergence
- Principal geodesic analysis
- Prior knowledge for pattern recognition
- Prisma (app)
- Probabilistic Action Cores
- Probabilistic context-free grammar
- Probabilistic latent semantic analysis
- Probabilistic soft logic
- Probability matching
- Probit model
- Product of experts
- Programming with Big Data in R
- Proper generalized decomposition
- Pruning (decision trees)
- Pushpak Bhattacharyya
- Q methodology
- Qloo
- Quality control and genetic algorithms
- Quantum Artificial Intelligence Lab
- Queueing theory
- Quick, Draw!
- R (programming language)
- Rada Mihalcea
- Rademacher complexity
- Radial basis function kernel
- Rand index
- Random indexing
- Random projection
- Random subspace method
- Ranking SVM
- RapidMiner
- Rattle GUI
- Raymond Cattell
- Reasoning system
- Regularization perspectives on support vector machines
- Relational data mining
- Relationship square
- Relevance vector machine
- Relief (feature selection)
- Renjin
- Repertory grid
- Representer theorem
- Reward-based selection
- Richard Zemel
- Right to explanation
- RoboEarth
- Robust principal component analysis
- RuleML Symposium
- Rule induction
- Rules extraction system family
- SAS (software)
- SNNS
- SPSS Modeler
- SUBCLU
- Sample complexity
- Sample exclusion dimension
- Santa Fe Trail problem
- Savi Technology
- Schema (genetic algorithms)
- Search-based software engineering
- Selection (genetic algorithm)
- Self-Service Semantic Suite
- Semantic folding
- Semantic mapping (statistics)
- Semidefinite embedding
- Sense Networks
- Sensorium Project
- Sequence labeling
- Sequential minimal optimization
- Shattered set
- Shogun (toolbox)
- Silhouette (clustering)
- SimHash
- SimRank
- Similarity measure
- Simple matching coefficient
- Simultaneous localization and mapping
- Sinkov statistic
- Sliced inverse regression
- Snakes and Ladders
- Soft independent modelling of class analogies
- Soft output Viterbi algorithm
- Solomonoff's theory of inductive inference
- SolveIT Software
- Spectral clustering
- Spike-and-slab variable selection
- Statistical machine translation
- Statistical parsing
- Statistical semantics
- Stefano Soatto
- Stephen Wolfram
- Stochastic block model
- Stochastic cellular automaton
- Stochastic diffusion search
- Stochastic grammar
- Stochastic matrix
- Stochastic universal sampling
- Stress majorization
- String kernel
- Structural equation modeling
- Structural risk minimization
- Structured sparsity regularization
- Structured support vector machine
- Subclass reachability
- Sufficient dimension reduction
- Sukhotin's algorithm
- Sum of absolute differences
- Sum of absolute transformed differences
- Swarm intelligence
- Switching Kalman filter
- Symbolic regression
- Synchronous context-free grammar
- Syntactic pattern recognition
- TD-Gammon
- TIMIT
- Teaching dimension
- Teuvo Kohonen
- Textual case-based reasoning
- Theory of conjoint measurement
- Thomas G. Dietterich
- Thurstonian model
- Topic model
- Tournament selection
- Training, test, and validation sets
- Transiogram
- Trax Image Recognition
- Trigram tagger
- Truncation selection
- Tucker decomposition
- UIMA
- UPGMA
- Ugly duckling theorem
- Uncertain data
- Uniform convergence in probability
- Unique negative dimension
- Universal portfolio algorithm
- User behavior analytics
- VC dimension
- VIGRA
- Validation set
- Vapnik–Chervonenkis theory
- Variable-order Bayesian network
- Variable kernel density estimation
- Variable rules analysis
- Variational message passing
- Varimax rotation
- Vector quantization
- Vicarious (company)
- Viterbi algorithm
- Vowpal Wabbit
- WACA clustering algorithm
- WPGMA
- Ward's method
- Weasel program
- Whitening transformation
- Winnow (algorithm)
- Win–stay, lose–switch
- Witness set
- Wolfram Language
- Wolfram Mathematica
- Writer invariant
- Xgboost
- Yooreeka
- Zeroth (software)
Further reading
- Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). [https://web.archive.org/web/20091110212529/http://www-stat.stanford.edu/~tibs/ElemStatLearn/ The Elements of Statistical Learning], Springer. {{ISBN|0-387-95284-5}}.
- Pedro Domingos (September 2015), The Master Algorithm, Basic Books, {{ISBN|978-0-465-06570-7}}
- Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012). [http://www.cs.nyu.edu/~mohri/mlbook/ Foundations of Machine Learning], The MIT Press. {{ISBN|978-0-262-01825-8}}.
- Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., {{ISBN|978-0-12-374856-0}}.
- David J. C. MacKay. [http://www.inference.phy.cam.ac.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] Cambridge: Cambridge University Press, 2003. {{ISBN|0-521-64298-1}}
- Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, {{ISBN|0-471-05669-3}}.
- Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. {{ISBN|0-19-853864-2}}.
- Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, {{ISBN|0-471-03003-1}}.
- Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
- Ray Solomonoff, "[http://world.std.com/~rjs/indinf56.pdf An Inductive Inference Machine]" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
References
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External links
{{Sister project links|Machine learning}}
- [https://web.archive.org/web/20170118041300/https://mitprofessionalx.mit.edu/courses/course-v1:MITProfessionalX+DSx+2016_T1/about Data Science: Data to Insights from MIT (machine learning)]
- Popular online course by Andrew Ng, at [https://www.coursera.org/course/ml Coursera]. It uses GNU Octave. The course is a free version of Stanford University's actual course taught by Ng, see.stanford.edu/Course/CS229 available for free].
- [https://mloss.org/ mloss] is an academic database of open-source machine learning software.
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