Sample Essay on Machine Learning

Machine learning refers to a branch in artificial intelligence that concerns the study and construction of systems that are capable of learning data. For instance, machine learning unit involved in email messaging can be trained to differentiate spam from non-spam messages. Such a unit can be used in classifying new emails into spam messages and non-spam messages and then put them in their respective folders after learning.

The central aspect of this learning is generalization and representation. Data representation on the basis of functions and instances forms the core of evaluation which is the central part of any machine learning system. Generalization refers to the property of a system to perform properly on instances of unseen data. This can be guaranteed on conditions that are important in the study of computational learning theory.

Basically, such learning as a scientific discipline tries to answer the question of how systems can be programmed to learn how they can automatically learn and improve performance with experience. It is important to note that in this context, learning does not refer to learning by the heart. Rather, it implies recognizing different patterns and making intelligent decisions on the basis of the available data.

The major challenge is that various decisions are possible based on the possible inputs that are difficult to describe. To address this issue, the learning field develops algorithms whose function is to discover knowledge from experience and data on the basis of computational and sound statistical principles. Many and different approaches of are integrated in machine learning. They include combinatorial optimization, probability theory, search, logic, reinforcement learning, statistics, and also control theory.

Methods that are developed are based on different applications. These range from vision to processing, language, forecasting, games, pattern recognition, expert systems, data mining and robotics. Since its inception, machine learning field has grown rapidly. Over the years, the learning field has grown from a mere work of few computer engineers to works that explore the ability of computers to learn how to play games as well as mimic how human brain functions.

The field has also evolved the field of statistics from one that ignored the computational considerations into a broad field that is important to the production of fundamental statistical-computational theories that are used in learning processes. There are also many algorithms used in new learning such as the Bayesian networks and vector machines that are used in the contemporary commercial systems.

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Sources

http://en.wikipedia.org/wiki/Machine_learning

http://www.mlplatform.nl/what-is-machine-learning/

http://www.springer.com/computer/ai/journal/10994

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