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Course, academic year 2017/2018
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Experimental Analysis of Algorithms - NTIN033
Czech title: Experimentální analýza algoritmů
Guaranteed by: Department of Distributed and Dependable Systems (32-KDSS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2014
Semester: summer
E-Credits: 6
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Additional information: http://d3s.mff.cuni.cz/teaching/ntin033
Guarantor: RNDr. Alena Koubková, CSc.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: Tajemník Katedry (22.04.2010)

Major goals, basic methods and software tools of experimental algorithmics. An illustration of using statistical methods in experimental studies of algorithm behavior. Sampling methods and simulation of data sets for experiments with algorithms. Individual students' project: the experimental study of some algorithm. Basic knowledge of statistics is required.
Course completion requirements - Czech
Last update: RNDr. Alena Koubková, CSc. (10.10.2017)

Zápočet se uděluje za vypracování semestrální práce, účast na cvičení se nevyžaduje. Charakter zápočtu umožňuje jeho opakování. Zápočet je nutnou podmínkou pro zkoušku.

Literature - Czech
Last update: RNDr. Alena Koubková, CSc. (04.04.2014)

Antoch, J., Vorlíčková, D.: Vybrané metody statistické analýzy dat. Academia, Praha, 1992.

Demetrescu, C., Italiano, G. F.: What do we learn from experimental algorithmics? Proc. MFCS´00, Lecture Notes in Comp. Sci. 1893, 36-51, Springer-Verlag 2000

Komárková, L., Komárek, A., Bína, V.: Základy analýzy dat a statistického úsudku s příklady v R. Oeconomia, Praha, 2007.

Requirements to the exam - Czech
Last update: RNDr. Alena Koubková, CSc. (10.10.2017)

Předmět nemá zkoušku v klasické podobě. Známka je odvozena od úrovně odevzdané semestrální práce.

Syllabus -
Last update: RNDr. Alena Koubková, CSc. (04.04.2014)

Experimental algorithmics and its relation to the theoretical analysis of algorithms.

Software systems for the experimental analysis of algorithms: libraries of efficient implementations, test sets (generators), tools for vizualization and animation.

A survey of statistical methods for working with experimental data: A random sample from normal distribution, parameter estimates and tests of hypotheses about parameters, methods for determination of the necessary sample size. Sequential analysis. Tests of normality, influence of non-normality on the results, robust methods. Tests of independence.

Obtaining data for experiments with algorithms, random generators, methods for sampling from existing collections.

Software systems for statistical data analysis.

Examples of experimental analysis of the time complexity of algorithms (sorting, graph algorithms, hashing and others), interpretation of results and comparison with theoretical results.

Individual elaboration of an experimental study of behavior of some algorithm (depending on the student's own choice).

 
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