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Last update: ROZ143 (15.11.2012)
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Last update: TAJ143 (03.12.2013)
Students will be able: To apply basic data mining tools to common problems (classification, regression, clustering). To choose algorithm suitable for the given problem and assess its accuracy. Understand how and why data mining and machine learning algorithms work. |
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Last update: TAJ143 (02.07.2013)
R:Larose D. T., Discovering Knowledge in Data: An Introduction to Data Mining, Wiley-Interscience, 2004, ISBN 0471666572 A:Ch. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007, ISBN 0387310738 A:I. H. Witten, Data Mining: Practical Machine Learning Tools, Morgan Kaufmann, 2011, ISBN 0123748569 |
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Last update: ROZ143 (15.11.2012)
Online course materials at http://ich.vscht.cz/~svozil/teaching.html Course (video and slides) "Learning from data" from Caltechu: http://work.caltech.edu/telecourse.html Course (video and slides) "Machine learning" from Stanford: http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1 and http://cs229.stanford.edu/materials.html Course "Machine learning" on Coursera: https://www.coursera.org/course/machlearning |
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Last update: Svozil Daniel prof. Mgr. Ph.D. (28.02.2011)
Zápočet bude získán na základě vypracovaného projektu, jehož součástí je závěrečné zpráva a prezentace výsledků. Na konci semestru studenti skládají písemnou zkoušku. |
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Last update: ROZ143 (15.11.2012)
1) Introduction to data mining. CRISP-DM. Data warehousing. OLAP. 2) Pattern recognition - basic concepts. Supervised/unsupervised learning. Classification and regression. Generalization. Overfitting. Bias-variance tradeoff. 3) Test set. Cross validation. k-nearest neighbors. 4) Cluster analysis. 5) Information theory. Decission trees. 6) Neural networks I. Threshold neuron. ADALINE. Linear perceptron. 7) Neural networks II. Multilayer Perceptron. 8) Neural networks III. Radial Basis Function (RBF) Networks. 9) Neural networks IV. Self-Organizing Map. 10) Support Vector Machines. 11) Genetic Algorithms. 12) Feature Selection. Feature Extraction. 13) Ensemble learning. 14) Summary. |
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Last update: TAJ143 (02.07.2013)
Fundamentals of Statistics |
Teaching methods | ||||
Activity | Credits | Hours | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 1 | 28 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1 | 28 | ||
3 / 5 | 84 / 140 |
Coursework assessment | |
Form | Significance |
Defense of an individual project | 30 |
Examination test | 70 |