Faculty of Information Technology, BUT

Course details

Advanced Methods of Analyses and Simulation

PMM IFET BUT RSPMO Acad. year 2018/2019 Summer semester 4 credits

Current academic year

The content of the subject is to make students familiar with the methods of analyses and simulation techniques (fuzzy logic, artificial neural networks, and genetic algorithms) by the way of explanation of the principles of these theories and their resulting applications in managerial practice.

Guarantor

Dostál Petr, prof. Ing., CSc. (II FBM BUT)

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 26 hrs exercises

Assessment points

100 exam

Lecturer

Dostál Petr, prof. Ing., CSc. (II FBM BUT)

Instructor

Šebestová Monika, Ing. (II FBM BUT)

Subject specific learning outcomes and competences

The obtained knowledge and skills of the subject will enable the graduates fine and modern access in the processes of analyses and simulation (in the national economy and private sector, organizations, firms, companies, banks, etc.) in order to support decision making in business focussed on problems of risk management.

Learning objectives

The aim of the course is to make students familiar with the methods of analyses and simulation techniques (fuzzy logic, artificial neural networks, and genetic algorithms) by the way of explanation of the principles of these theories and their resulting applications in decision making in business focussed to problems of risk management.

Prerequisite kwnowledge and skills

The knowledge in the area of math (linear algebra, arrays, analyses of functions, operations with matrixes) statistics (analysis of time series, regression analyses, the use of statistical methods in the economy).

Corequisite knowledge and skills

Risk engineering.

Syllabus of lectures

  1. Introduction and definition of subject "Advanced methods of analyses and simulation
  2. Identification of basic terms in the area of analyses
  3. Identification of basic terms and fuzzy logic rules, the build-up of models
  4. Presentation of case studies on the use of fuzzy logic in risk management
  5. Identification of basic terms in the area of artificial neural networks
  6. Presentation of case studies on the use of artificial neural networks in risk management
  7. Identification of basic terms in the area of genetic algorithms
  8. Presentation of case studies on the use of genetic algorithms in risk management
  9. Methods of simulation of prediction by means of fuzzy logic, neural networks and genetic algorithms
  10. Introduction to the theory of chaos and possible usage in risk management
  11. The usage of software means for solving problems
  12. Introduction to problems of simulation
  13. Presentation of applications of the usage of simulation in risk management

Progress assessment

The participation in lectures is not checked. The participation in training is compulsory and is systematically checked. The students are supposed to excuse for their absence. The teacher judges the reason of excuse. The way of substitution of a missed training will be set by the teacher individually.

Attendance form: 80% face-to face, 20% distance learning.
Attendance in seminars will be checked, a student has to fulfil at least 75% attendance in seminars.
The absence in seminars could be recompense with a special assignment or with a special exam test.

Controlled instruction

Teaching is carried out through lectures and seminars. Lectures consist of interpretations of basic principles, the methodology of a given discipline, problems and their exemplary solutions. Seminars particularly support practical mastery of subject matter presented in lectures or assigned for individual study with the active participation of students.

Exam: written test
Exercise: Attendance (absences at seminars can be replaced with spare tasks and written tests). Submission of the seminar paper.

Exam prerequisites

Attendance in seminars (75 %). Submission of seminar paper.

Course inclusion in study plans

  • Programme IT-MSC-2, field MMI, any year of study, Compulsory-Elective group N
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