Mathematical methods for data analysis
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
11MMOA | Z,ZK | 4 | 2P+2C+12B | Czech |
- Course guarantor:
- Ivan Nagy, Pavla Pecherková, Evženie Uglickich
- Lecturer:
- Pavla Pecherková, Evženie Uglickich
- Tutor:
- Pavla Pecherková, Evženie Uglickich
- 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:
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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:
- Time-table for winter semester 2024/2025:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- PL nav.prez.22/23 (pro program PL) (compulsory course)
- PL nav.kombi 22/23 (pro program PL) (compulsory course)
- PL nav.prez.23/24 (pro program PL) (compulsory course)
- PL nav.kombi 23/24 (pro program PL) (compulsory course)