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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Eperimental Data Analysis

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Code Completion Credits Range Language
B2M31AEDA Z,ZK 6 2P+2C Czech
Corequisite:
Safety in Electrical Engineering for a master´s degree (BEZM)
Lecturer:
Jan Rusz (guarantor)
Tutor:
Jan Rusz (guarantor), Martin Kaňok
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:
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:
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:
Time-table for winter semester 2019/2020:
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
Fri
Thu
roomT2:A3-413a
Rusz J.
12:45–14:15
(lecture parallel1)
Dejvice
Laborator K413A
roomT2:A4-405
Rusz J.
Kaňok M.

14:30–16:00
(lecture parallel1
parallel nr.101)

Dejvice
Laborator
Fri
Time-table for summer semester 2019/2020:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-10-18
For updated information see http://bilakniha.cvut.cz/en/predmet5598606.html