Introduction
Machine Learning[6][8][12] is concerned with the study of building computer programs that automatically improve and/or adapt their performance through experience. Machine learning can be thought of as “programming by example” [11]. Machine learning has many common things with other domains such as statistics and probability theory (understanding the phenomena that have generated the data), data mining (finding patterns in the data that are understandable by people) and cognitive sciences (human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people such as concept learning, skill acquisition, strategy change, etc.) [1].
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References
Dietterich, T.G.: Machine Learning. In: Nature Encyclopedia of Cognitive Science. Macmillan, London (2003)
http://www.onlinefreeebooks.net/free-ebooks-computer-programming-technology/artificial-intelligence/artificial-intelligence-course-material-pdf.html (accessed on February 10, 2011)
Jordan, M.: An introduction to Graphical Models, Center for Biological and Computational Learning, Massachusetts Institute of Technology (1997), http://www.cis.upenn.edu/~mkearns/papers/barbados/jordan-tut.pdf (accessed on February 10, 2011)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley and Sons, New York (2001)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Machine Learning course, University of Cambridge, UK, http://learning.eng.cam.ac.uk/zoubin/ml06/index.html (accessed on February 10, 2011)
Kearns, M.J., Vazirani, U.V.: An introduction to computational learning theory. MIT Press, Cambridge (1994)
Vapnik, V.N.: The nature of statistical learning theory. Springer, Heidelberg (1995)
Vapnik, V.N.: Statistical learning theory. Wiley and Sons, Chichester (1998)
Foundations of machine learning theory course, Princeton University, http://www.cs.princeton.edu/courses/archive/spr03/cs511/ (accessed on February 10, 2011)
http://www-it.fmi.uni-sofia.bg/markov/courses/ml.html#IntroductiontoMachineLearning (accessed on February 10, 2011)
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Grosan, C., Abraham, A. (2011). Machine Learning. In: Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21004-4_10
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