 CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024

# Eperimental Data Analysis

Code Completion Credits Range Language
B2M31AEDA Z,ZK 6 2P+2C Czech

Garant předmětu:
Jan Rusz
Lecturer:
Jan Rusz
Tutor:
Petr Krýže, Jan Rusz, Martin Šubert
Supervisor:
Department of Circuit Theory
Synopsis:

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.

Requirements:

The basic knowledge of Matlab software.

Syllabus of lectures:

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 &amp; 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

Syllabus of tutorials:

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 &amp; 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

Study Objective:

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.

Study materials:

 Vidakovic B. Statistics for bioengineering sciences: with Matlab and WinBUGS support. New Yourk: Springer, 2011.

 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.

Note:
Further information:
https://moodle.fel.cvut.cz/courses/B2M31AEDA
Time-table for winter semester 2023/2024:
 06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00 roomT2:A3-413a12:45–14:15(lecture parallel1parallel nr.103)DejviceLaborator K413AroomT2:A3-413aRusz J.14:30–16:00(lecture parallel1parallel nr.101)DejviceLaborator K413AroomT2:A3-413aRusz J.16:15–17:45(lecture parallel1parallel nr.102)DejviceLaborator K413A roomT2:C3-135Rusz J.12:45–14:15(lecture parallel1)DejviceT2:C3-135
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2023-11-30
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet5598606.html