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 building and studying algorithms that can learn from data and make predictions about the data. Such algorithms work by building a model from sample inputs in order to make predictions or data-driven decisions: 2 instead of following strictly static program instructions. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Machine learning is closely related to and often overlaps with computational statistics; a discipline also specialized in the formulation of forecasts. It has strong links to mathematical optimization, which provides methods, theory and application domains to the field. Machine learning is used in a number of computing tasks where designing and programming explicit algorithms is not feasible. Example applications include spam filtering, optical character recognition (OCR), search engines, and computer vision. Machine learning is sometimes confused with data mining, although this focuses more on exploratory data analysis. Machine learning and pattern recognition “can be seen as two aspects of the same field.” When used in industrial settings, machine learning methods can be referred to as predictive analytics or predictive modeling. 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 has provided a more formal, widely cited definition: “A computer program is said to learn from experience E with respect to some class of tasks and performance measures P, if its performance on the tasks in T, measured by P, improves with experience E”. This definition is notable because it defines machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in his article "Computing Machinery and Intelligence" to replace the question "Can machines think?" “Can machines do what we (as thinking entities) can do?” Types of Problems and Tasks Machine learning tasks are generally classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are: Supervised learning: The computer is presented with example inputs and desired outputs, provided by a "teacher", and the goal is to learn a general rule that maps the inputs to the outputs. Unsupervised learning: No labels are assigned to the learning algorithm, leaving it alone to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means to an end. Reinforcement learning: A computer program interacts with a dynamic environment in which it must achieve a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether or not it has gotten closer to its goal. Another example is learning to play by playing against an opponent. Between supervised and unsupervised learning is semi-supervised learning, in which the teacher provides an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle in which the entire set of instances of the.