SylabUZ
| Course name | Expert systems |
| Course ID | 11.3-WE-INFD-ExpSyst-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | Computer Science |
| Education profile | academic |
| Level of studies | Second-cycle Erasmus programme |
| Beginning semester | winter term 2022/2023 |
| Semester | 3 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Credit with grade |
| Laboratory | 30 | 2 | - | - | Credit with grade |
To familiarize with the basics of construction, operation and types of expert systems.
To familiarize with the different methods of artificial intelligence, types of knowledge bases and foundations of their creation.
To form basic skills in designing, building and running expert systems.
Principles of programming, Algorithms and data structures.
Ideas of the modelling of intellectual acts of the man. Intelligent systems and their differentiation. Artificial intelligence tendencies. Interpretation of notions information, knowledge.
Expert systems. Structure of expert system. Categories of expert systems. Properties of expert systems. Expert systems design. Methods of the expert system design.
Knowledge acquisition. Knowledge acquisition from experts. Knowledge acquisition from databases.
Knowledge base of expert system. Rule representation of the knowledge. Knowledge base design. Knowledge base verification.
Exact knowledge evaluation in expert systems. Forward reasoning. Backward reasoning.
Cases based reasoning.
Machine learning. Notions and definitions. Strategies of machine learning
The interface of the communication the user-the system. Graphic user interface. Dialogue design. Explanations system.
Approximate representation of the knowledge. Forms of knowledge uncertainty. Fuzzy sets basics.
Approximate knowledge processing. Fuzzyfication and defuzzyfication. Fuzzy reasoning. Other forms of artificial intelligence General characterization of artificial neural networks.
General characterization of genetic algorithm. The evolution of systems of artificial intelligence.
Hybrid structures. Development tendencies. Selected tools and program libraries for building expert systems.
Integration of expert systems with control-measurement systems, databases and WWW.
Lecture, consultation, laboratory exercises, team work, discussion.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture - the main condition to get a pass are sufficient marks in written or oral tests conducted at least once per semester.
Laboratory – a condition of the credit is the obtainment of affirmative estimations all laboratory exercises.
Calculation of the final grade: lecture 40% + laboratory 60%
1. Hand D., Mannila H., Smyth P.: Principles of Data Mining, MIT Press, 2001
2. Siler W., Buckley J., Fuzzy Expert Systems and Fuzzy Reasoning, John Wiley & Sons, 2005
3. Larase D.: Discovering Knowledge in Data. An Introduction to Data Mining, John Wiley & Sons, 11 lut 2005
4. Giarratano J., Riley G., Expert systems: principles and programming, Thomson Course Technology, 2005
1. Gallant S., Neutral network learning and expert systems, MIT Press, 1993
2. Korbicz J., Koscielny J., Kowalczuk Z., Cholewa W. Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer-Verlag, 2004
3. Schalkof R., Intelligent Systems: Principles, Paradigms and Pragmatics, Joness and Bartlet, 2011
Modified by dr inż. Robert Szulim (last modification: 08-04-2022 09:35)
