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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.^[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] ML involves the study and construction of algorithms that can learn from and make predictions on data.^[3] 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

Machine learning hardware

Machine learning tools

Comparison of deep learning software

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
MLJ.jl – A machine learning framework for Julia

Machine learning libraries

Machine learning algorithms

Machine learning methods

Instance-based algorithm

Meta-learning

Reinforcement learning

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

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

Unsupervised learning

Artificial neural networks

Artificial neural network

Association rule learning

Hierarchical clustering

Cluster analysis

Anomaly detection

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

Semi-supervised learning

Deep learning

Machine learning research

History of machine learning

Timeline of machine learning

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)
ML4ALL (Machine Learning For All)

Machine learning publications

Books on machine learning

Machine learning journals

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

Persons influential in machine learning

References

^ http://www.britannica.com/EBchecked/topic/1116194/machine-learning This tertiary source reuses information from other sources but does not name them.
^ Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN 978-1-118-63817-0.
^ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915.

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.