Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computer learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn and make predictions about data. Such algorithms work by building a model from sample inputs in order to make predictions or decisions based on data:2 rather than following strictly static program instructions. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay. Machine learning is closely related to and often overlaps with computer statistics; a discipline that also specializes in making predictions. It has close links with mathematical optimization, which bring methods, theories and areas of application to the field. Machine learning is used in a range of computing tasks for which the design and programming of explicit algorithms is impossible. Example applications include spam filtering, optical character recognition (OCR), search engines, and computer vision. Machine learning is sometimes confused with data mining, although the latter focuses more on exploratory data analysis. Machine learning and pattern recognition “can be seen as two facets of the same field.” When used in industrial settings, machine learning methods may be called 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 provided a more formal, widely cited definition: "A computer program is said to learn from experience E with respect to a certain class of tasks and measures its performance P, if its performance on tasks of T , as measured by P, improves. with experience E”. This definition is notable for defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposition in his article "Computing Machinery and Intelligence" that the question "Can machines think ? » be replaced by the question “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, based 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 their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: no label is given to the learning. algorithm, leaving it alone to find the structure of 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 it has gotten closer to its goal or not. Another example is learning to play a game by playing against an opponent. Between supervised learning and.