638-0804/02 – Mathematical Tools of Informatics (MPI)
| Gurantor department | Department of Automation and Computing in Industry | Credits | 7 |
| Subject guarantor | doc. Ing. Jiří David, Ph.D. | Subject version guarantor | doc. Ing. Jiří David, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2004/2005 | Year of cancellation | 2020/2021 |
| Intended for the faculties | FMT | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Student will be able to determine a mine of errors and errors type at the numerical computing.
Student will be able to principles the modern optimization methods and determine a procedure solution with utilization the Genetic Algorithms and the Evolutional Algorithms.
Student will get an overview of the basic principles of the datamining metods and of the basic acquirements at solution numerical probléme with utilization the Matlab and with utilization the Matlab Toolbox Genetic Algorithm.
Teaching methods
Lectures
Tutorials
Summary
Subjekt put mind to the questions solution numerical problems. Students do one's homework the to determine a mine of errors and errors type at the numerical computing, to principles the modern optimization methods and determine a procedure solution with utilization the Genetic Algorithms and the Evolutional Algorithms and get an overview of the basic principles of the datamining metods and of the basic acquirements at solution numerical problems with utilization the Matlab and with utilization the Matlab Toolbox Genetic Algorithm.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
General tutorial to problems diploma work.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Errors, sources and types errors. Rounding error. Errors of method.
2. Incomplete numbers and number representation in computer. Correctitude, conditionality and stability numerical problems.
3. Optimalization problems. Classification of optimization methods.
4. Principles of basic of optimization methods. Evolutional methods.
5. Principle of genetic algorithm.
6. Fitness value. Code of strings.
7. Termination of genetic algorithm. Stagnation of genetic algorithm.
8. Selection of strings. Principles of particular method of selection.
9. Crossing. Mutation. Types of mutation
10. Variants of genetic algoritm.
11. Principle of evolutional strategy. Principle of differential evolution. Principle of SOMA. Principle of UIS.
12. Data warehouse.
13. Data mining. Data mining problems.
14. Principle of methodology CRISP- DM.
15. Data miningu methods . Principle of decision - making trees.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction