Decision Support Systems

Prerequisite:

Occupational Safety II KM (35BP2)

The course is a substitute for:

Decision Support Systems (X33SPR)

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

Decision Support Systems (DSS) integrates methods, which are based on results of classical mathematic disciplines (ie. statistics), with knowledge - based methods that are typical for Artificial Intelligence. Lectures are rooted in through explanation of knowledge-based systems design and principles. They are also concentrated on related theoretical and application scopes of knowledge engineering. Extra attention is devoted to practical using methods for data analysis. In the end of lectures the feature of Knowledge Discovery in Databases (KDD) is introduced.

Requirements:

Syllabus of lectures:

1. Overview of intelligent decision support systems

2. Basic problems of intelligent decision making

3. Expert systems - types and principles

4. Means for knowledge representation

5. Uncertainty handling

6. Diagnostic expert systems - examples and using

7. Elements of knowledge engineering

8. Application of pattern recognition and machine learning methods for creating a knowledge base

9. Utilisation of data analysis methods in knowledge acquisition I

10. Utilisation of data analysis methods in knowledge acquisition II

11. Knowledge Discovery in Databases (KDD)

12. Planning expert systems

13. Structured and unstructured information

14. Decision making supported by model-based simulation

Syllabus of tutorials:

1. Demonstration of a simple diagnostic expert system

2. Simple task solving

3. Seminar on elementary methods for knowledge representation

4. Seminar on elementary methods for knowledge representation

5. Demonstration of selected machine learning algorithms

6. Simple task solving

7. Demonstration of elementary pattern recognition algorithms

8. Simple task solving

9. Demonstration of an data warehouse

10. Simple task solving

11. Demonstration of a geographical information system

12. Simple task solving

13. Demonstration of decision making based on simulation

14. Simple task solving

Study Objective:

Study materials:

[1] Hájek, P., Havránek, T., Jiroušek, R.: Uncertain Information Processing in Expert Systems. CRC Press, Inc. 1992

[2] Neapolitan, R. E.: Probabilistic Reasoning in Expert Systems: Theory and Algorithms. John Willey & Sons, New York, 1989

[3] Weiss, S. M.: Predictive Data Mining - A Practical Guide. Morgan Kaufmann Publishers, Inc., San Francisco, 1998

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Technická kybernetika-inženýrský blok (compulsory course)
• Biomedicínské inženýrství - inženýrský blok (compulsory course)
• Biomedicínské inženýrství - inženýrský blok (compulsory course)
• Technická kybernetika-inženýrský blok (compulsory course)