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Students learn to prepare raw data for further processing and analysis. They learn what algorithms can be used to extract parameters from various data sources, such as images, texts, time series, etc., and learn the skills to apply these theoretical concepts to solve a specific problem in individual projects - e.g., parameter extraction from image data or from Internet.
Last update: Jirát Jiří (10.01.2014)
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R:Pyle, D. ''Data Preparation for Data Mining''. Morgan Kaufmann, 1999. ISBN 1558605290. R:Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. A. ''Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)''. Springer, 2006. ISBN 3540354875. Last update: Jirát Jiří (10.01.2014)
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1. Data exploration, exploratory analysis techniques, visualization of raw data. 2. Descriptive statistics. 3. Methods to determine the relevance of features. 4. Problems with data ? dimensionality, noise, outliers, inconsistency, missing values, non-numeric data. 5. Data cleaning, transformation, imputing, discretization, binning. 6. Reduction of data dimension. 7. Reduction of data volume, class balancing. 8. Feature extraction from text. 9. Feature extraction from documents, web. Preprocessing of structured data. 10. Feature extraction from time series. 11. Feature extraction from images. 12. Data preparation case studies. 13. Automation of data preprocessing. Last update: Jirát Jiří (10.01.2014)
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https://edux.fit.cvut.cz/courses/MI-PDD/ (login necessary) Last update: Jirát Jiří (10.01.2014)
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Students will be able to: Apply knowledge of algorithms for extraction of parameters from various data sources as a fundamental part of knowledge engineering, Last update: Jirát Jiří (31.01.2014)
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Fundamentals of statistics, FCD course in data mining. Last update: Jirát Jiří (10.01.2014)
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Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Práce na individuálním projektu | 2.2 | 61 | ||
Účast na seminářích | 0.5 | 14 | ||
4 / 4 | 103 / 112 |