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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Eperimental Data Analysis

The course is not on the list Without time-table
Code Completion Credits Range Language
B2M31AED Z,ZK 5 2P+2C Czech
Corequisite:
Lecturer:
Jan Rusz (guarantor)
Tutor:
Jan Rusz (guarantor), Jan Hlavnička
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:

Basics of Matlab.

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.

[3] Meloun M, Militký J. Statistická analýza experimentálních dat. Praha: Academia, 2004.

Note:
Further information:
http://sami.fel.cvut.cz/aed/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2020-05-30
For updated information see http://bilakniha.cvut.cz/en/predmet4636206.html