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Topics covered within the program include information technologies and applications; data mining, data analytics, and data visualization; practices for the collection, integration, analysis, and presentation of business data and information; and data ethics and security. Students will also be provided with the opportunity to apply these core business analytics skills to industry-specific cases

Bregman Divergences and Extensions to Mixed Data Sets Cecile Levasseur, Brandon Burdge, Ken Kreutz-Delgado University of California, San Diego Jacobs School of Engineering La Jolla, California, USA fclevasseur, bburdge, kreutzgucsd.edu Uwe F. Mayer University of Utah Department of Mathematics Salt Lake City, Utah, USA mayermath.utah.edu Abstract We present a general

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Fundamentals of Data Mining. An ever-increasing volume of research and industry data is being collected on a daily basis. Skilled data scientists are needed to process and filter the data, to detect new patterns or anomalies within the data, and gain deeper insight from the data.

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Data Mining with R by Luis Torgo ISBN: 9781482234893. Publication Date: 2017-01-19 . Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes

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This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model

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