User:The Transhumanist/Sandbox51

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

Machine learning – subfield of computer science (more particularly soft computing) that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. 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?

 * 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

Subfields of machine learning
Subfields of machine learning
 * Computational learning theory – studying the design and analysis of machine learning algorithms.
 * Grammar induction
 * Meta learning

Cross-disciplinary fields involving machine learning
Cross-disciplinary fields involving machine learning
 * Adversarial machine learning
 * Predictive analytics
 * Quantum machine learning
 * Robot learning
 * Developmental robotics

Applications of machine learning
Applications of machine learning
 * Biomedical informatics
 * Computer vision
 * Customer relationship management –
 * Data mining
 * Email filtering
 * Inverted pendulum – balance and equilibrium system.
 * Natural language processing
 * Automatic summarization
 * Automatic taxonomy construction
 * Dialog system
 * Grammar checker
 * Language recognition
 * Handwriting recognition
 * Optical character recognition
 * Speech recognition
 * Machine translation
 * Question answering
 * Speech synthesis
 * Text simplification
 * Pattern recognition
 * Facial recognition system
 * Handwriting recognition
 * Image recognition
 * Optical character recognition
 * Speech recognition
 * Recommendation system
 * Search engine

Machine learning hardware
Machine learning hardware
 * Graphics processing unit
 * Tensor processing unit
 * Vision processing unit

Machine learning tools
Machine learning tools  (list)
 * Comparison of deep learning software
 * Comparison of deep learning software/Resources

Machine learning frameworks
Machine learning framework

Proprietary machine learning frameworks
Proprietary machine learning frameworks
 * Amazon Machine Learning
 * Microsoft Azure Machine Learning Studio
 * DistBelief – replaced by TensorFlow
 * Microsoft Cognitive Toolkit

Open source machine learning frameworks
Open source machine learning frameworks
 * Apache Singa
 * Caffe
 * H2O
 * MLPACK
 * TensorFlow
 * Torch
 * Accord.Net

Machine learning libraries
Machine learning library  (list)
 * Deeplearning4j
 * Theano
 * Scikit-learn

Machine learning algorithms
Machine learning algorithm

Types of machine learning algorithms

 * Almeida–Pineda recurrent backpropagation
 * ALOPEX
 * Almeida–Pineda recurrent backpropagation
 * Backpropagation
 * Bootstrap aggregating
 * CN2 algorithm
 * Constructing skill trees
 * Dehaene–Changeux model
 * Diffusion map
 * Dominance-based rough set approach
 * Dynamic time warping
 * Error-driven learning
 * Evolutionary multimodal optimization
 * Expectation–maximization algorithm
 * FastICA
 * Forward–backward algorithm
 * GeneRec
 * Genetic Algorithm for Rule Set Production
 * Growing self-organizing map
 * HEXQ
 * 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
 * Loss functions for classification
 * Manifold alignment
 * 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
Machine learning method  (list)
 * 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 (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 (GBRT)
 * Gradient boosting machine (GBM)
 * Random Forest
 * Stacked Generalization (blending)

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
 * AODE
 * Association rule learning algorithms
 * Apriori algorithm
 * Eclat algorithm
 * 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
 * Information fuzzy networks (IFN)
 * Conditional Random Field
 * ANOVA
 * Quadratic classifiers
 * k-nearest neighbor
 * Boosting
 * SPRINT
 * Bayesian networks
 * Naive Bayes
 * Hidden Markov models

Artificial neural network
Artificial neural network
 * Autoencoder
 * Backpropagation
 * Boltzmann machine
 * Convolutional neural network
 * Deep learning
 * Hopfield network
 * Multilayer perceptron
 * Perceptron
 * Radial basis function network (RBFN)
 * Restricted Boltzmann machine
 * Recurrent neural network (RNN)
 * Self-organizing map (SOM)
 * Spiking neural network

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

Artificial neural networks
Artificial neural network
 * Feedforward neural network
 * Extreme learning machine
 * 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 algorithm
 * K-means clustering
 * K-medians
 * Mean-shift
 * OPTICS algorithm

Anomaly detection
Anomaly detection
 * k-nearest neighbors classification (k-NN)
 * Local outlier factor

Semi-supervised learning
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.
 * 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
 * Deep Boltzmann Machine (DBM)
 * 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
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

Machine learning organizations
Machine learning organizations
 * Knowledge Engineering and Machine Learning Group

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)

Books on machine learning
Books about machine learning

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
 * 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
 * Jacek M. Zurada
 * Jaime Carbonell
 * 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