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The name machine learning was coined in 1959 by Arthur Samuel. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies. 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. 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. When used interactively, these can be presented to the user for labeling.
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. A support vector machine is a classifier that divides its input space into two regions, separated by a linear boundary. Here, it has learned to distinguish black and white circles. This is typically tackled in a supervised way. 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. Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “Machine Learning” in 1959 while at IBM. 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 1980, expert systems had come to dominate AI, and statistics was out of favor. Neural networks research had been abandoned by AI and computer science around the same time. CS field, as “connectionism”, by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.
Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Machine learning and statistics are closely related fields. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein “algorithmic model” means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. A core objective of a learner is to generalize from its experience.
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has underfit the data. If the complexity of the model is increased in response, then the training error decreases.
In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Decision tree learning uses a decision tree as a predictive model, which maps observations about an item to conclusions about the item’s target value. Association rule learning is a method for discovering interesting relations between variables in large databases. Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing.
Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Several learning algorithms, mostly unsupervised learning algorithms, aim at discovering better representations of the inputs provided during training. Classical examples include principal components analysis and cluster analysis. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional.