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2. Outline of machine learning - Wikipedia

   en.wikipedia.org/wiki/Outline_of_machine_learning

   The following outline is provided as an overview of and topical guide to machine learning: Machine learning – a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. [1] In 1959, Arthur Samuel defined machine learning as a "field of ...

3. Machine learning - Wikipedia

   en.wikipedia.org/wiki/Machine_learning

   e. 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 ...

4. Neural network (machine learning) - Wikipedia

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

   v. t. e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [ 1 ][ 2 ] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.

5. Timeline of machine learning - Wikipedia

   en.wikipedia.org/wiki/Timeline_of_machine_learning

   1950s. Pioneering machine learning research is conducted using simple algorithms. 1960s. Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s. ' AI winter ' caused by pessimism about machine learning effectiveness. 1980s.

6. Deep reinforcement learning - Wikipedia

   en.wikipedia.org/wiki/Deep_reinforcement_learning

   [25] [26] Another class of model-free deep reinforcement learning algorithms rely on dynamic programming, inspired by temporal difference learning and Q-learning. In discrete action spaces, these algorithms usually learn a neural network Q-function Q ( s , a ) {\displaystyle Q(s,a)} that estimates the future returns taking action a ...

7. Boosting (machine learning) - Wikipedia

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

   Machine learningand data mining. In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. [1] It is used in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. [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. Online machine learning - Wikipedia

   en.wikipedia.org/wiki/Online_machine_learning

   Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...