Experimental 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 & 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 & 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:
-
[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.
- 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
Mon Tue Wed Thu Fri - Time-table for summer semester 2023/2024:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Medical electronics and bioinformatics (compulsory elective course)
- Electronics and Communications - Electronics (compulsory elective course)
- Electronics and Communications - Audiovisual Technology and Signal Processing (compulsory elective course)
- Electronics and Communications - Photonics (compulsory elective course)
- Electronics and Communications - Technology of the Internet of Things (compulsory elective course)
- Electronics and Communications - Radio Communications and Systems (compulsory elective course)
- Medical electronics and bioinformatics (compulsory elective course)
- Medical electronics and bioinformatics (compulsory elective course)
- Medical electronics and bioinformatics (compulsory elective course)
In order to register for the course B2M31AEDA, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
During a review of study plans, the course B2M31AED can be substituted for the course B2M31AEDA.