- Department of Applied Mathematics
Students are introduced to the basic methods of discovering knowledge in data. In particular, they learn the basic techniques of data preprocessing, multidimensional data visualization, statistical techniques of data transformation, and fundamental principles of knowledge discovery methods. Students will be aware of the relationships between model bias and variance, and know the fundamentals of assessing model quality. Data mining software is extensively used in the module. Students will be able to apply basic data mining tools to common problems (classification, regression, clustering).
- Syllabus of lectures:
1. Introduction to data mining, data preparation, data visualization. Statistical analysis of data.
2. Data model, nearest neighbour classifier. Training, validation and testing, model's quality evaluation.
3. Artificial neural networks in data mining. Unsupervised neural networks - competitive learning
4. Probability and Bayesian classification. Decision trees and rules.
5. Neural networks with supervised learning. Cluster analysis.
6. Combining neural networks and models in general. Data mining in the Clementine environment.
7. Text mining, Web mining, selected applications, new trends.
- Syllabus of tutorials:
1. Data, visualization, statistics. Statistical analysis of data. Data preprocessing, dimension reduction, relevance of inputs. Model, learning, testing, model validation. Data mining process, classification, prediction, modeling.
2. Cluster analysis, SOM. Project assignment  Consultations, working on projects.  Presentations of results, workshop. Assessment.
- Study Objective:
The module aims to introduce students to a rapidly developing field - knowledge discovery in data.
- Study materials:
1. Larose, D. T. ''Discovering Knowledge in Data: An Introduction to Data Mining''. Wiley-Interscience, 2004. ISBN 0471666572.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans:
- Information Technology (Presented in Czech), Version 2014 (elective course)
- Computer Science (Presented in Czech), Version 2014 (compulsory course of the specialization, elective course)
- Bc. Programme Informatics, Part -Time Form of Study, in Czech, Version 2015 - 2019 (VO)
- Bc Branch Security and Information Technology, Part-Time Form, in Czech, Version 2015 to 2019 (elective course)
- Bc.Branch WSI, Specialization Software Engineering, Part-Time Form, Versionverze 2015 - 2019 (elective course)