Machine learning Wikipedia. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term Machine Learning in 1. IBM3. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,4 machine learning explores the study and construction of algorithms that can learn from and make predictions on data5 such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,6 2 through building a model from sample inputs. Machine learning is employed 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 or malicious insiders working towards a data breach,7optical character recognition OCR,8learning to rank, and computer vision. Machine learning is closely related to and often overlaps with computational statistics, which also focuses on prediction making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,9 where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised1. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results and uncover hidden insights through learning from historical relationships and trends in the data. According to the Gartner hype cycle of 2. Effective machine learning is difficult because finding patterns is hard and often not enough training data is available as a result, machine learning programs often fail to deliver. OvervieweditTom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field 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. Ibm Spss Modeler 14.1' title='Ibm Spss Modeler 14.1' />This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turings proposal in his paper Computing Machinery and Intelligence, in which the question Can machines think is replaced with the question Can machines do what we as thinking entities can do. In Turings proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed. Types of problems and taskseditMachine learning tasks are typically classified into two broad categories, depending on whether there is a learning signal or feedback available to a learning system 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. As special cases, the input signal can be only partially available, or restricted to special feedback. Semi supervised learning the computer is given only an incomplete training signal a training set with some often many of the target outputs missing. Active learning the computer can only obtain training labels for a limited set of instances based on a budget, and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling. Reinforcement learning training data in form of rewards and punishments is given only as feedback to the programs actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent. 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. Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning, generates its own sequences also called curriculum of learning situations to cumulatively acquire repertoires of novel skills through autonomous self exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. Another categorization of machine learning tasks arises when one considers the desired output of a machine learned system 6 3. In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more multi label classification of these classes. This is typically tackled in a supervised way. SPSS/3.jpg?1' alt='Ibm Spss Modeler 14.1' title='Ibm Spss Modeler 14.1' />Spam filtering is an example of classification, where the inputs are email or other messages and the classes are spam and not spam. In regression, also a supervised problem, the outputs are continuous rather than discrete. In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task. Density estimation finds the distribution of inputs in some space. Dimensionality reduction simplifies inputs by mapping them into a lower dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics. History and relationships to other fieldseditAs a scientific endeavour, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed neural networks these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. However, an increasing emphasis on the logical, knowledge based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1. 98. 0, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolicknowledge based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. Latest SAS Certified Statistical Business Analyst Using SAS 9 Regression and Modeling A00240 Certification Sample Questions and Online Practice Test with Exam. King Arthur Ps2 Game. BIG DATA and HEALTH ANALYTICS EDITED BY. KATHERINE MARCONI The Graduate School University of Maryland University College. HAROLD LEHMANN School of Medicine The Johns.