Intro To Machine Learning

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Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.

Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. 

 Such algorithms operate by building a modelfrom example inputs in order to make data-driven predictions or decisions, 2 rather than following strictly static program instructions. 

Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. 

Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. 

Example applications include spam filtering, optical character recognition (OCR),[4] search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis.[6] Machine learning and pattern recognition “can be viewed as two facets of the same field.”[3]:vii When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling. 

 Overview In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Tom M. Mitchell provided a widely quoted, more formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”.

This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in his pap

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