Machine learning

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Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that computers use to perform tasks without explicit instructions. Instead, these systems rely on patterns and inference to learn from data.

Overview[edit | edit source]

Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Types of Machine Learning[edit | edit source]

Machine learning tasks are typically classified into several broad categories:

  • Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

Applications[edit | edit source]

Machine learning is used in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.

See Also[edit | edit source]

References[edit | edit source]


Machine learning Resources
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