Machine learning information

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.^[1] Recently, artificial neural networks have been able to surpass many previous approaches in performance.^[2]^[3]

ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.^[4]^[5] When applied to business problems, it is known under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.

The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis (EDA) through unsupervised learning.^[7]^[8]

From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning.

^ The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). "Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming". Artificial Intelligence in Design '96. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9. ISBN 978-94-010-6610-5.
^ "What is Machine Learning?". IBM. Retrieved 2023-06-27.
^ Zhou, Victor (2019-12-20). "Machine Learning for Beginners: An Introduction to Neural Networks". Medium. Archived from the original on 2022-03-09. Retrieved 2021-08-15.
^ Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad (2020). "Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning". IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423. doi:10.1109/tvt.2020.3034800. ISSN 0018-9545. S2CID 228989788.
^ Yoosefzadeh-Najafabadi, Mohsen; Hugh, Earl; Tulpan, Dan; Sulik, John; Eskandari, Milad (2021). "Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean?". Front. Plant Sci. 11: 624273. doi:10.3389/fpls.2020.624273. PMC 7835636. PMID 33510761.
^ Cite error: The named reference bishop2006 was invoked but never defined (see the help page).
^ Machine learning and pattern recognition "can be viewed as two facets of the same field".^[6]^: vii
^ Friedman, Jerome H. (1998). "Data Mining and Statistics: What's the connection?". Computing Science and Statistics. 29 (1): 3–9.

[1] The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). "Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming". Artificial Intelligence in Design '96. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9. ISBN 978-94-010-6610-5.

[ibm-2] "What is Machine Learning?". IBM. Retrieved 2023-06-27.

[:6-3] Zhou, Victor (2019-12-20). "Machine Learning for Beginners: An Introduction to Neural Networks". Medium. Archived from the original on 2022-03-09. Retrieved 2021-08-15.

[tvt-4] Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad (2020). "Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning". IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423. doi:10.1109/tvt.2020.3034800. ISSN 0018-9545. S2CID 228989788.

[:7-5] Yoosefzadeh-Najafabadi, Mohsen; Hugh, Earl; Tulpan, Dan; Sulik, John; Eskandari, Milad (2021). "Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean?". Front. Plant Sci. 11: 624273. doi:10.3389/fpls.2020.624273. PMC 7835636. PMID 33510761.

[bishop2006-6] Cite error: The named reference bishop2006 was invoked but never defined (see the help page).

[7] Machine learning and pattern recognition "can be viewed as two facets of the same field".^[6]^: vii

[:9-8] Friedman, Jerome H. (1998). "Data Mining and Statistics: What's the connection?". Computing Science and Statistics. 29 (1): 3–9.

Machine learning information

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Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Curriculum learning Rule-based learning 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)
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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 Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net 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
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