Subject description - B2M31AEDA

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B2M31AEDA Eperimental Data Analysis
Roles:PV Extent of teaching:2P+2C
Department:13131 Language of teaching:CS
Guarantors:Rusz J. Completion:Z,ZK
Lecturers:Rusz J. Credits:6
Tutors:Kaňok M., Rusz J. Semester:Z

Anotation:

In the course of subject "Experimental Data Analysis", students will acquire knowledge regarding fundamental methods for data analysis and machine learning for evaluation and interpretation of data. In the course of practical lectures, students will solve individual tasks using real data from signal processing in neuroscience research. In the course of semestral project, student will solve complex task and present obtained results. The aim of the subject is to introduce practical application of fundamental statistical methods as well as to teach students to use critical thinking and to acquire additional knowledge in solution of practical tasks.

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:

Program Branch Role Recommended semester
MPEK2_2018 Audiovisual and Signal Processing PV 3
MPEK1_2018 Electronics PV 3
MPEK3_2018 Photonics PV 3
MPEK7_2018 Radio Systems PV 3


Page updated 3.8.2020 09:51:43, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)