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B2M31AEDA	Experimental Data Analysis
Roles:	PV	Extent of teaching:	2P+2C
Department:	13131	Language of teaching:	CS
Guarantors:	Rusz J.	Completion:	Z,ZK
Lecturers:	Rusz J.	Credits:	6
Tutors:	Krýže P., Rusz J., Šubert M.	Semester:	Z

Web page:

https://moodle.fel.cvut.cz/courses/B2M31AEDA

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:

Students will acquire knowledge regarding fundamental methods for data analysis and machine learning for evaluation and interpretation of data.

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.

Requirements:

The basic knowledge of Matlab software.

Keywords:

Data analysis and interpretation, statistics, machine learning.

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MPBIO4_2018	Signal processing	PV	–
MPEK4_2018	Technology of the Internet of Things	PV	3
MPEK1_2018	Electronics	PV	3
MPBIO2_2018	Medical Instrumentation	PV	–
MPEK3_2018	Photonics	PV	3
MPBIO1_2018	Bioinformatics	PV	–
MPBIO3_2018	Image processing	PV	–
MPEK2_2018	Audiovisual and Signal Processing	PV	3
MPEK7_2018	Radio Communications and Systems	PV	3

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Page updated 20.4.2024 09:51:18, semester: Z/2023-4, Z/2024-5, L/2023-4, Send comments about the content to the Administrators of the Academic Programs

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