Artificial Intelligence and Expert Systems
Software Engineering
Faculty of Engineering
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Artificial Intelligence and Expert Systems | YZM406 | Compulsory | Bachelor's degree | 4 | Spring | 4 |
Name of Lecturer(s)
Associate Prof. Dr. Mehmet Zeki KONYAR
Learning Outcomes of the Course Unit
1) The student defines the concepts of artificial intelligence.
2) Learns fuzzy logic, genetic algorithm logic.
3) xpert system designs an intelligent system using techniques such as neural networks.
4) Explain the various applications of artificial intelligence.
5) Understands Artificial Intelligence Searches
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | Middle | Middle | Middle | No relation | No relation | No relation | No relation | Middle | Low | High | High | High | |
| 2 | Middle | Middle | Middle | No relation | No relation | No relation | No relation | Middle | Low | High | High | High | |
| 3 | Middle | Middle | Middle | No relation | No relation | No relation | No relation | Middle | Low | High | High | High | |
| 4 | Middle | Middle | Middle | No relation | No relation | No relation | No relation | Middle | Low | High | High | High | |
| 5 | Middle | Middle | Middle | No relation | No relation | No relation | No relation | Middle | Low | High | High | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Basic principles of artificial intelligence, Expert system, Information Engineering, General structure of expert system, Information presentation methods, Search methods, Inference, Design of Expert Systems, Advanced chaining, Back chaining, Probability and expert systems, Fuzzy sets, Properties of fuzzy sets, Fuzzy set operations Fuzzy relationships, Membership functions, Fuzziness, Inference techniques, Rinsing techniques, Artificial neural networks, Artificial Neural Network applications, Fuzzy Artificial Neural Networks, Genetic Algorithm.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
English
Work Placement(s)
Not Required