| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Intelligent Systems | BLM420 | Elective | Bachelor's degree | 4 | Spring | 5 |
Name of Lecturer(s)
Prof. Dr. Nevcihan DURU
Prof. Dr. Kadir ERKAN
Prof. Dr. Ahmet SAYAR
Prof. Dr. Mehmet YILDIRIM
Associate Prof. Dr. Alev MUTLU
Lecturer Uğur YILDIZ
Learning Outcomes of the Course Unit
1) Evaluate various types of intelligent systems
2) Specify the principles of knowledge representation
3) Synthesize the expert system techniques and logic, particularly as related to knowledge representation and decision support system.
4) Describe how knowledge representations can be manipulated to solve problems in a knowledge based systems context
5) Analyze the use cases of intelligent and expert systems.
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | No relation | Low | No relation | No relation | No relation | No relation | Middle | No relation | No relation | High | No relation | No relation | |
| 2 | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | No relation | High | No relation | No relation | |
| 3 | Low | No relation | Low | No relation | No relation | No relation | Middle | No relation | No relation | High | No relation | No relation | |
| 4 | No relation | Low | Middle | No relation | No relation | No relation | Middle | No relation | No relation | Middle | No relation | No relation | |
| 5 | No relation | No relation | No relation | No relation | No relation | No relation | Middle | No relation | No relation | High | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
There is no prerequisite course
Course Contents
Expert Systems (ES): major characteristics of expert systems; techniques; rule-based expert systems; knowledge acquisition; applications, Fuzzy Logic (FL), Neural Networks (NS), Genetic Algorithms (GA)
Weekly Schedule
1) Introduction to Intelligent Systems2) Classical Approaches to the Design and Development of Intelligent Systems
3) Classical Approaches to the Design and Development of Expert Systems -2
4) Artificial Intelligence (AI) history and applications
5) Intelligent agents : Searching and problem solving agents
6) Search and constraint satisfaction
7) Heuristic and advanced search
8) Midterm examination/Assessment
9) First order logic
10) Knowledge representation and knowledge base
11) Planning Systems
12) Reasoning in uncertain situations
13) Machine learning
14) Languages and programming techniques for AI (Prolog, Lisp)
15) Languages and programming techniques for AI (Prolog, Lisp)
16) Final examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Simulation
3) Case Study
4) Problem Solving
5) Project Based Learning
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required