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Mathematical Methods for Data Analysis

Course Code:
11MMAD
Academic Degree:
master
Study Programme:
Intelligent Transport Systems (N1041A040007)
semesterlanguage
3czech flag
Form of Study:
full-time
Credits:
6
Number of Hours:
3 + 3 hours per week - in full-time study
Type of Course:
obligatory
Course Completion:
credit, exam
Supervisor:
doc. Ing. Ivan NAGY, CSc.
Course Tutor:
 
roh  Lectures:
doc. Ing. Evženie Uglickich, CSc.
roh  Training Course:
doc. Ing. Evženie Uglickich, CSc.
Supervising Department:
Department of Applied Mathematics (16111)
Keywords:
Modelling, estimation, prediction, classification, filtration, control, k-means, fuzzy, decision tree, support vector machine
Abstract:
Stocastic modelling, estimation, prediction, filtration, control, methods of data analysis: k-means, DBSCAN, naive Bayes, decision trees, support vector machine.
Objectives:
To teach students the basic of dynamic statistic, i. e. analysis of data evolving in time. The accent is given to methods of clustering ans classification. Theoretical exposision of the subject is closely bound with a practical realization of the tasks in computer.