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Lecturer(s)
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Course content
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unspecified
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Learning activities and teaching methods
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Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Independent creative and artistic activities, Individual consultation, Seminár
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Learning outcomes
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The course covers methods and algorithms of contemporary advanced data analysis for applied research, development and design of new products and processes, technology optimization, quality improvement and discovery support. The course is based on classical multivariate statistical methods and regression models of complex engineering and experimental systems. It continues with an introduction to current methods of effective mathematical and statistical methods for extracting information from data and for supporting optimal engineering and managerial decision-making. Methods include generalized regression models, regularized and robust regression, nonlinear models, cluster analysis, neural networks, support vector machines, and regression trees. All models and methods are accompanied by examples, applications or simulations. Knowledge and use of these methods are an essential prerequisite for current research and for competitive, sustainable and successful technologies and engineering applications.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral exam
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Recommended literature
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R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical, 2012.
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DRAPER, N.R., SMITH, H. Applied Regression Analysis. New York: J. Wiley, 1998. ISBN 978-0-471-17082-2.
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HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J. The Elements of Statistical Learning. New York: Springer-Verlag, 2009. ISBN 978-0-387-84857-0.
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KUPKA, K. Darwin. Definice a popis jazyka. Pardubice: TriloByte, 2011.
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MELOUN M., MILITKÝ, J. Interaktivní statistická analýza dat. Praha: Karolinum, 2012. ISBN 978-8-024-62173-9.
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