Outline of machine learning

From Wikipedia, the free encyclopedia

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Deep learning Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Boltzmann machine Restricted GAN Diffusion model SOM Convolutional neural network U-Net LeNet AlexNet DeepDream Neural radiance field Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Journals and conferences ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
v t e

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.

v t e Differentiable computing
General	Differentiable programming Information geometry Statistical manifold Automatic differentiation Neuromorphic computing Pattern recognition Ricci calculus Computational learning theory Inductive bias
Hardware	IPU TPU VPU Memristor SpiNNaker
Software libraries	TensorFlow PyTorch Keras scikit-learn Theano JAX Flux.jl MindSpore
Portals Computer programming Technology

v t e Wikipedia outlines
General reference Culture and the arts Geography and places Health and fitness History and events Mathematics and logic Natural and physical sciences People and self Philosophy and thinking Religion and belief systems Society and social sciences Technology and applied sciences

Categories:

Hidden categories: