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2. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   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. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

3. Quantum machine learning - Wikipedia

   en.wikipedia.org/wiki/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. quantum-enhanced machine learning.

4. Feature learning - Wikipedia

   en.wikipedia.org/wiki/Feature_learning

   Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly, compare transfer learning. In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature ...

5. Rule-based machine learning - Wikipedia

   en.wikipedia.org/wiki/Rule-based_machine_learning

   v. t. e. Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [1] [2] [3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...

6. Reinforcement learning - Wikipedia

   en.wikipedia.org/wiki/Reinforcement_learning

   Reinforcement learning ( RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...

7. Deep learning - Wikipedia

   en.wikipedia.org/wiki/Deep_learning

   Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]

8. Feature (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Feature_(machine_learning)

   In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. [1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features ...

9. Active learning (machine learning) - Wikipedia

   en.wikipedia.org/wiki/Active_learning_(machine...

   e. Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...