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What is machine learning?

5 cited papers · March 16, 2026 · Powered by Researchly AI

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Machine learning (ML) is a computational paradigm that has emerged alongside big data technologies and high-performance computing to create new opportunities fo…

Machine learning (ML) is a computational paradigm that has emerged alongside big data technologies and high-performance computing to create new opportunities for data-intensive science. Λιάκος et al. (2018)1A core assumption of traditional ML methodologies is that training data and testing data are drawn from the same domain, sharing the same input feature space and data distribution characteristics. Weiss et al. (2016)2
1
Machine Learning in Agriculture: A ReviewΚωνσταντίνος Λιάκος, Patrizia Busato et al.2018Sensors
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2
A survey of transfer learningKarl R. Weiss, Taghi M. Khoshgoftaar et al.2016Journal Of Big Data
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  • Machine Learning — A set of techniques enabling systems to learn from data, used across domains such as crop management, livestock management, water management, and soil management, evolving farm systems into real-time AI-enabled decision support programs. Λιάκος et al. (2018)
1
  • Deep Learning (DL) — A subfield of ML that uses multiple deep layers of units with highly optimised algorithms and architectures, making a significant impact in various domains.
23Alzubaidi et al. (2021)2
  • Transfer Learning — A methodology where high-performance learners are trained using data from different domains, addressing scenarios where training data is expensive or difficult to collect.
4Weiss et al. (2016)4
  • Feature Selection — A dimensionality reduction technique that selects a small subset of relevant features by removing irrelevant, redundant, or noisy ones, leading to higher learning accuracy and better model interpretability. Miao & Niu (2016)
1
Machine Learning in Agriculture: A ReviewΚωνσταντίνος Λιάκος, Patrizia Busato et al.2018Sensors
View
2
Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
View
3
Review of Deep Learning Algorithms and ArchitecturesAjay Shrestha, Ausif Mahmood2019IEEE Access
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4
A survey of transfer learningKarl R. Weiss, Taghi M. Khoshgoftaar et al.2016Journal Of Big Data
View
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Diagram
Raw Data
 |
 v
[Feature Selection / Preprocessing]
 |
 v
[ML Model (e.g., Deep Neural Network)]
 |
 +--> Training Data --> [Learning Algorithm]
 | |
 | v
 | [Trained Model]
 | |
 v v
Test Data ---------> [Predictions / Recommendations]
Deep learning has outperformed well-known ML techniques in many domains.1Alzubaidi et al. (2021)1Feature selection algorithms, particularly unsupervised ones, have been shown experimentally to improve the performance of clustering and other ML tasks.2Miao & Niu (2016)2
1
Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
View
2
A Survey on Feature SelectionJianyu Miao, Lingfeng Niu2016Procedia Computer Science
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Traditional ML methodologies assume that training and testing data come from the same domain; when this assumption does not hold, model performance can degrade significantly, motivating the need for transfer learning.1Weiss et al. (2016)1
1
A survey of transfer learningKarl R. Weiss, Taghi M. Khoshgoftaar et al.2016Journal Of Big Data
View
  • ML has created new opportunities for data-intensive science by combining with big data technologies and high-performance computing. Λιάκος et al. (2018)
1
  • Deep learning achieves outstanding results on complex cognitive tasks, sometimes matching or beating human performance.
2Alzubaidi et al. (2021)3
  • Transfer learning addresses the challenge of scarce or expensive training data by leveraging knowledge from related domains.
4Weiss et al. (2016)4
  • Feature selection improves learning accuracy, reduces computational cost, and enhances model interpretability.
5Miao & Niu (2016)5
1
Machine Learning in Agriculture: A ReviewΚωνσταντίνος Λιάκος, Patrizia Busato et al.2018Sensors
View
2
Review of Deep Learning Algorithms and ArchitecturesAjay Shrestha, Ausif Mahmood2019IEEE Access
View
3
Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
View
4
A survey of transfer learningKarl R. Weiss, Taghi M. Khoshgoftaar et al.2016Journal Of Big Data
View
5
A Survey on Feature SelectionJianyu Miao, Lingfeng Niu2016Procedia Computer Science
View
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  1. "Supervised vs unsupervised vs reinforcement learning — key differences and applications"
  2. "Deep learning architectures: CNN, RNN, and Transformer models compared"
  3. "Applications of machine learning in agriculture and precision farming"

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