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B2M31AED	Experimental Data Analysis
Roles:		Extent of teaching:	2P+2C
Department:	13131	Language of teaching:	CS
Guarantors:		Completion:	Z,ZK
Lecturers:		Credits:	5
Tutors:		Semester:	Z

Web page:

http://sami.fel.cvut.cz/aed/

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.

Study targets:

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.

Content:

The course is composed of three parts. The first part involves basic statistical analysis, the second part shows how to practically use advanced statistical methods and the third part introduces usage of basic methods of machine learning.

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

Requirements:

Basics of Matlab.

Keywords:

Statistics; data analysis; Matlab; models.

Subject is included into these academic programs:

Program

Branch

Role

Recommended semester

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Page updated 26.4.2024 05:51:27, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)