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1. Machine learning: Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to ... (Study of algorithms that improve automatically through experience) [100%] 2023-12-30 [Machine learning] [Cybernetics]...
2. Machine learning: Machine learning (ML) it is the study of computer algorithms that may improve automatically via experience and the usage of data. It is considered to be a subset of artificial intelligence. [100%] 2023-12-29 [Machine learning] [Cybernetics]...
3. Machine learning: Machine learning is a set of techniques and algorithms that allow computer programs to learn simple or complex tasks by analyzing some training data (or examples of how they should behave). Some believe machine learning is the first stage in ... [100%] 2023-12-29 [Technology] [Artificial intelligence]...
4. Machine learning: Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. Machine learning algorithms build a model based on sample ... (Study of algorithms that improve automatically through experience) [100%] 2023-12-12 [Machine learning] [Cybernetics]...
5. Machine learning: Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A major focus of machine learning research ... [100%] 2023-12-31 [Machine learning]
6. Machine learning: ML Machine learning is concerned with modifying the knowledge representation structures (or knowledge base) underlying a computer program such that its problem-solving capability improves (for surveys, cf. , ). (Mathematics) [100%] 2023-10-24
7. Machine learning: Machine learning methods automatically learn statistical regularities in a training data set to make accurate predictions about new data. Two definitions are: For example, a machine learning algorithm for Machine translation may be presented with several thousand examples of sentences ... [100%] 2024-01-21
8. Machine learning potential: Beginning in the 1990s, researchers have employed machine learning programs to construct interatomic potentials, mapping atomic structures to their potential energies. Such machine learning potentials promised to fill the gap between density functional theory, a highly-accurate but computationally-intensive ... [81%] 2023-12-29 [Machine learning] [Materials science]...
9. Machine learning control: Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with machine learning methods. Key applications are complex nonlinear systems for which linear control theory methods are not ... (Subfield of machine learning, intelligent control, and control theory) [81%] 2025-07-03 [Machine learning] [Control theory]...
10. Machine learning control: Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods of machine learning. Key applications are complex nonlinear systems for which linear control theory methods are not applicable. (Subfield of machine learning, intelligent control and control theory) [81%] 2025-07-03 [Machine learning] [Control theory]...
11. Machine learning in bioinformatics: Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems ... [70%] 2024-01-10 [Machine learning] [Bioinformatics]...
12. Machine learning in bioinformatics: Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems ... [70%] 2024-01-10 [Machine learning] [Bioinformatics]...
13. Machine learning in physics: Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. (Applications of machine learning to quantum physics) [70%] 2024-01-10 [Machine learning] [Quantum information science]...
14. Machine learning in physics: Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. (Engineering) [70%] 2024-01-09 [Machine learning] [Quantum information science]...
15. Machine learning in physics: Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. (Physics) [70%] 2024-05-04 [Machine learning] [Quantum information science]...
16. Machine learning in earth sciences: Applications of machine learning in earth sciences include geological mapping, gas leakage detection and geological features identification. Machine learning (ML) is a type of artificial intelligence (AI) that enables computer systems to classify, cluster, identify and analyze vast and complex ... (None) [63%] 2024-04-11 [Machine learning] [Geological techniques]...
17. Machine learning in earth sciences: Applications of machine learning in earth sciences include geological mapping, gas leakage detection and geological features identification. Machine learning (ML) is a type of artificial intelligence (AI) that enables computer systems to classify, cluster, identify and analyze vast and complex ... (None) [63%] 2024-08-30 [Machine learning] [Geological techniques]...
18. Machine learning in video games: Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of ... (Overview of the use of machine learning in several video games) [63%] 2025-07-03 [Machine learning] [Game artificial intelligence]...
19. Active learning (machine learning): Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also ... (Machine learning) [85%] 2023-08-30 [Machine learning]
20. Active learning (machine learning): Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also ... (Machine learning) [85%] 2023-11-10 [Machine learning]
21. Learning curve (machine learning): In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for a training set against this loss function evaluated on a validation data set with same parameters as produced the optimal ... (Machine learning) [85%] 2023-07-14 [Model selection] [Machine learning]...
22. Learning curve (machine learning): In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for a training set against this loss function evaluated on a validation data set with same parameters as produced the optimal ... (Machine learning) [85%] 2023-04-20 [Model selection] [Machine learning]...
23. Weka (machine learning): Weka is a software tool for applying machine learning algorithms to data. Weka contains implementations of a collection algorithms and is written in Java. (Machine learning) [81%] 2023-06-27
24. Quantum machine learning: Quantum machine learning is the integration of quantum algorithms within machine learning programs.Cite error: Closing missing for tag The first letter refers to whether the system under study is classical or quantum, while the second letter defines whether a ... (Engineering) [81%] 2023-09-15 [Machine learning] [Quantum information science]...
25. Quantum machine learning: Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. (Interdisciplinary research area at the intersection of quantum physics and machine learning) [81%] 2023-12-29 [Machine learning] [Quantum information science]...
26. Margin (machine learning): In machine learning the margin of a single data point is defined to be the distance from the data point to a decision boundary. Note that there are many distances and decision boundaries that may be appropriate for certain datasets ... (Machine learning) [81%] 2023-11-28 [Support vector machines]
27. Pythia (machine learning): Pythia is an ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. It was created by Yannis Assael, Thea Sommerschield, and Jonathan Prag, researchers from Google DeepMind and the University of Oxford. (Machine learning) [81%] 2021-12-21 [Machine learning] [Artificial intelligence]...
28. Tanagra (machine learning): Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature ... (Software) [81%] 2022-11-14 [Data mining and machine learning software] [Free science software]...
29. Hyperparameter (machine learning): In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process, hence the name hyperparameter. This is in contrast to parameters which determine the model itself. (Machine learning) [81%] 2023-12-19 [Machine learning] [Model selection]...
30. Leakage (machine learning): In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which would not be expected to be available at prediction time, causing the predictive scores (metrics ... (Machine learning) [81%] 2023-09-14 [Machine learning] [Statistical classification]...
31. Feature (machine learning): In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. (Machine learning) [81%] 2023-09-25 [Data mining] [Machine learning]...
32. Logic learning machine: Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm, developed by Marco Muselli, Senior Researcher at the Italian National Research Council ... (Machine learning method) [81%] 2023-12-12 [Classification algorithms] [Machine learning algorithms]...
33. Online machine learning: In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning ... (Method of machine learning) [81%] 2023-12-12 [Machine learning algorithms]
34. Fairness (machine learning): Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on ... (Machine learning) [81%] 2023-10-01 [Machine learning] [Computing and society]...
35. Adversarial machine learning: Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems ... (Research field that lies at the intersection of machine learning and computer security) [81%] 2023-09-15 [Machine learning] [Computer security]...
36. Fairness (machine learning): Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on ... (Machine learning) [81%] 2023-12-22 [Machine learning] [Information ethics]...
37. Boosting (machine learning): Das Wigan Casino war ein englischer Nachtclub in Wigan in Greater Manchester und bestand zwischen Mitte der 1960er Jahre und 1981. Der Nachtclub wurde ab 1971 als Spielstätte für die vor allem in Nordengland und den Midlands aktive Musikbewegung Northern ... (Machine learning) [81%] 2024-01-20 [Classification algorithms] [Object recognition and categorization]...
38. Torch (machine learning): Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms implemented in C. (Software) [81%] 2023-11-16 [Free statistical software] [Software using the BSD license]...
39. GloVe (machine learning): GloVe, coined from Global Vectors, is a model for distributed word representation. The model is an unsupervised learning algorithm for obtaining vector representations for words. (Machine learning) [81%] 2023-12-25 [Computational linguistics]
40. Automated machine learning: Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. (Process of automating the application of machine learning) [81%] 2023-08-22 [Machine learning]