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Information about Machine Learning

For the journal, see Machine Learning (journal).


As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.

The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related to data mining and statistics but also theoretical computer science.

Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data are to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:
  • Supervised learning — in which the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate) the behavior of a function which maps a vector into one of several classes by looking at several input-output examples of the function.
  • Unsupervised learning — which models a set of inputs: labeled examples are not available.
  • Semi-supervised learning — which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
  • Reinforcement learning — in which the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
  • Transduction — similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and test inputs which are available while training.
  • Learning to learn — in which the algorithm learns its own inductive bias based on previous experience.
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

Machine learning topics

This list represents the topics covered on a typical machine learning course.
Approximate inference techniques
Optimization
  • Most of methods listed above either use optimization or are instances of optimization algorithms
Meta-learning (ensemble methods)
Inductive transfer and learning to learn

See also

Bibliography

  • Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0262012111
  • Christopher M. Bishop (2007) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8.
  • Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, ISBN 0-935382-05-4.
  • Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, ISBN 0-934613-00-1.
  • Yves Kodratoff, Ryszard S. Michalski (1990), Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, ISBN 1-55860-119-8.
  • Ryszard S. Michalski, George Tecuci (1994), Machine Learning: A Multistrategy Approach, Volume IV, Morgan Kaufmann, ISBN 1-55860-251-8.
  • Bhagat, P. M. (2005). Pattern Recognition in Industry, Elsevier. ISBN 0-08-044538-1.
  • Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
  • Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
  • Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7 http://learning-from-data.com.
  • KECMAN Vojislav (2001), LEARNING AND SOFT COMPUTING, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8 http://support-vector.ws.
  • MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1.
  • Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7.
  • Ian H. Witten and Eibe Frank "Data Mining: Practical machine learning tools and techniques" Morgan Kaufmann ISBN 0-12-088407-0.
  • Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5.
  • Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
  • Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0387952845 (companion book site).
  • Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0471030031.

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Machine Learning

Discipline Machine learning
Language English
Publication details
Publisher Springer (USA)
Publication history 1986 to present
Indexing
ISSN 1573-0565
Links
* Journal homepage Machine Learning
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artificial intelligence (or AI) is "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.
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In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite list of well-defined instructions for accomplishing some task that, given an initial state, will proceed through a well-defined series of successive states, eventually terminating in an
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Induction or inductive reasoning, sometimes called inductive logic, is the process of reasoning in which the premises of an argument are believed to support the conclusion but do not ensure it. It is used to ascribe properties or relations to types based on tokens (i.
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Deductive reasoning, according to many dictionaries[1][2][3][4], is the type of reasoning that proceeds from general principles or premises to derive particular information.
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Data mining can be defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data".[1] Data mining may also be defined as "the science of extracting useful information from large data sets or databases".
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Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities.
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Theoretical computer science is the collection of topics of computer science that focuses on the more abstract, logical and mathematical aspects of computing, such as the theory of computation, analysis of algorithms and semantics of programming languages.
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Natural language processing (NLP) is a subfield of artificial intelligence and computational linguistics. It studies the problems of automated generation and understanding of natural human languages.
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Syntactic pattern recognition or structural pattern recognition is a form of pattern recognition, where items are presented pattern structures which can take into account more complex interrelationships between features than simple numerical feature vectors used in
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search engine is an information retrieval system designed to help find information stored on a computer system. Search engines help to minimize the time required to find information and the amount of information which must be consulted, akin to other techniques for managing
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In general, diagnosis (plural diagnoses) has two distinct dictionary definitions. The first definition is "the recognition of a disease or condition by its outward signs and symptoms", while the second definition is "the analysis of the underlying physiological/biochemical
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Bioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level.
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Cheminformatics (also known as chemoinformatics and chemical informatics) is the use of computer and informational techniques, applied to a range of problems in the field of chemistry.
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Credit card fraud is a wide-ranging term for theft and fraud committed using a credit card or any similar payment mechanism as a fraudulent source of funds in a transaction. The purpose may be to obtain goods without paying, or to obtain unauthorized funds from an account.
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A stock market is a market for the trading of company stock, and derivatives of same; both of these are securities listed on a stock exchange as well as those only traded privately.
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DNA sequence or genetic sequence is a succession of letters representing the primary structure of a real or hypothetical DNA molecule or strand, with the capacity to carry information.
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Speech recognition (in many contexts also known as automatic speech recognition, computer speech recognition or erroneously as voice recognition) is the process of converting a speech signal to a sequence of words in the form of digital data, by means of an
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Handwriting recognition is the ability of a computer to receive intelligible handwritten input. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition).
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Computer vision is the science and technology of machines that see.

As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images.
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Computer vision is the science and technology of machines that see.

As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images.
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strategy games of all time.]]

A strategy game is a game (e.g. computer, video or board game) in which the players' decision-making skills have a high significance in determining the outcome.
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Robot locomotion is the study of how to design robot appendages and control mechanisms to allow robots to move fluidly and efficiently. What might theoretically seem a simple matter like negotiating stairs has proved rather difficult in practice.
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Scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. It is based on gathering observable, empirical and measurable evidence subject to specific principles of reasoning,[1]
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Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name "Bayesian" comes from the frequent use of Bayes' theorem in the inference process.
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In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite list of well-defined instructions for accomplishing some task that, given an initial state, will proceed through a well-defined series of successive states, eventually terminating in an
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For the science of classifying living things, see .


Taxonomy is the practice and science of classification. The word comes from the Greek τάξις, taxis, 'order' +
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Supervised learning is a machine learning technique for creating a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs.
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Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc) and based on a training set of previously labeled
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Unsupervised learning is a method of machine learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no a priori output. In unsupervised learning, a data set of input objects is gathered.
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