Mathematical Methods for Data Analysis

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

Stocastic modelling, estimation, prediction, filtration, control, methods of data analysis: k-means, DBSCAN, naive Bayes, decision trees, support vector machine.

Requirements:

Probability, classical statistics, basics of algebra and calculus

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

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.

Study materials:

William M. Bolstad: Introduction to Bayesian Statistics, 2nd Edition. Willey, ISBN-13: 978-0470141151

P.Tan, M.Steinbach, V.Kumar: Introduction to Data Mining. Pearson Education, Inc., 2006. ISBN 0-321-32136-7

materiály na webu: http://staff.utia.cas.cz/suzdaleva/

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master Full-Time IS (CS) from 2022/23 (compulsory course)
• Master Full-Time IS (CS) from 2023/24 (compulsory course)