Subject description - BE2M31AED
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Instructions
| BE2M31AED |
Experimental Data Analysis |
| Roles: | |
Extent of teaching: | 2P+2C |
| Department: | 13131 |
Language of teaching: | EN |
| Guarantors: | |
Completion: | Z,ZK |
| Lecturers: | |
Credits: | 5 |
| Tutors: | |
Semester: | Z |
Anotation:
V rámci předmětu Analýza experimentálních dat si studenti ověří aplikace základních DSP metod na různých úlohách a rovněž budou aplikovat základní statistické a klasifikační metody pro vyhodnocení a interpretaci dat. V rámci semestrální práce budou studenti zpracovávat a vyhodnocovat reálná data, a na závěr prezentovat výsledky jejich práce. Cílem předmětu je naučit studenty kriticky myslet a získat dovedností při samostatném řešení praktických úkolů.
Course outlines:
| 1. | | Introduction to the subject "Experimental Data Analysis", introduction to data |
| 2. | | Introduction to the statistics, probability distributions, and plotting statistical data |
| 3. | | Hypothesis testing, group differences, paired test, effect size |
| 4. | | Correlations, normality of data testing, parametric vs. non-parametric tests |
| 5. | | Analysis of variance, post-hoc testing |
| 6. | | Type I & Type II errors, multiple comparisons, sample size estimation |
| 7. | | Factorial analysis of variance |
| 8. | | Introduction to models, regression analysis |
| 9. | | Supervised classification |
| 10. | | Model validation |
| 11. | | Unsupervised classification |
| 12. | | Dimensionality reduction, data interpretation |
| 13. | | Reserve, consultation of semestral projects |
| 14. | | Presentation of obtained results |
Exercises outline:
| 1. | | Introduction to Matlab |
| 2. | | Introduction to the statistics, probability distributions, and plotting statistical data |
| 3. | | Hypothesis testing, group differences, paired test, effect size |
| 4. | | Correlations, normality of data testing, parametric vs. non-parametric tests |
| 5. | | Analysis of variance, post-hoc testing |
| 6. | | Type I & Type II errors, multiple comparisons, sample size estimation |
| 7. | | Factorial analysis of variance |
| 8. | | Introduction to models, regression analysis |
| 9. | | Supervised classification |
| 10. | | Model validation |
| 11. | | Unsupervised classification |
| 12. | | Dimensionality reduction, data interpretation |
| 13. | | Reserve, consultation of semestral projects |
| 14. | | Presentation of obtained results |
Literature:
| [1] | | Vidakovic B. Statistics for bioengineering sciences: with Matlab and WinBUGS support. New Yourk: Springer, 2011. |
| [2] | | Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning : data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer, 2001. |
Requirements:
Subject is included into these academic programs:
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| Page updated 26.4.2024 09:51:38, semester: L/2023-4, Z/2024-5, Z/2023-4, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |