Information Package / Course Catalogue
Expert Systems
Course Code: MTK641
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

The purpose of this course is to introduce and to understand the significance of expert systems in artificial intelligence , and the course aims to gain the ability of researching using modern approaches in this field in both theoretically and practically.

Course Content

Overview of Artificial Intelligence and Expert Systems, Knowledge Representation, Rule-Based Systems, Associative Nets and Frame Systems, Logic Programming, Representing Uncertainty, Knowledge Acquisition, Heuristic Classification, Constructive Problem Solving, Machine Learning, Belief Networks, Case-based reasoning, Tools for Building Expert Systems.

Name of Lecturer(s)
Learning Outcomes
1.Ability to understand the expert systems
2.Ability to use the expert system design tools
3.Ability to design expert systems
4.To be able to gain the skill of interpreting some interrelations among these concepts
5.To be able to use mathematical concepts in solving certain types of problems
Recommended or Required Reading
1.Introduction to Expert System, P. Jackson, Addison-Wesley Publishing Company, ISBN 0201876868, 1998
Weekly Detailed Course Contents
Week 1 - Theoretical
Overview of Artificial Intelligence. What are Expert Systems?
Week 1 - Preparation Work
Introduction to Expert System, pp.1-37 should be read
Week 2 - Theoretical
Knowledge Representation
Week 2 - Preparation Work
Reading Introduction to Expert System, pp.38-56 should be read
Week 3 - Theoretical
Rule-Based Systems
Week 3 - Preparation Work
Reading Introduction to Expert System, pp.76-99 should be read
Week 4 - Theoretical
Associative Nets and Frame Systems
Week 4 - Preparation Work
Reading Introduction to Expert System, pp.100-115 should be read
Week 5 - Theoretical
Logic Programming
Week 5 - Preparation Work
Reading Introduction to Expert System, pp.143-161 should be read
Week 6 - Theoretical
Representing Uncertainty
Week 6 - Preparation Work
Reading Introduction to Expert System, pp.166-179 should be read
Week 7 - Theoretical
Knowledge Acquisition
Week 7 - Preparation Work
Reading Introduction to Expert System, pp.182-198 should be read
Week 8 - Theoretical
Heuristic Classification
Week 8 - Preparation Work
Reading Introduction to Expert System, pp.207-239 should be read
Week 9 - Theoretical
Constructive Problem Solving, Midterm exam
Week 9 - Preparation Work
All subjects covered
Week 10 - Theoretical
Constructive Problem Solving
Week 10 - Preparation Work
Reading Introduction to Expert System, pp.259-289 should be read
Week 11 - Theoretical
Tools for Building Expert Systems
Week 11 - Preparation Work
Reading Introduction to Expert System, pp.320-340 should be read
Week 12 - Theoretical
Machine Learning
Week 12 - Preparation Work
Reading Introduction to Expert System, pp.380-398 should be read
Week 13 - Theoretical
Belief Networks
Week 13 - Preparation Work
Reading Introduction to Expert System, pp.402-410 should be read
Week 14 - Theoretical
Case-based reasoning
Week 14 - Preparation Work
Reading Introduction to Expert System, pp.413-425 should be read
Week 15 - Preparation Work
All subjects covered
Week 15 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Term Assignment1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Assignment1066
Term Project1066
Reading140570
Midterm Examination130232
Final Examination142244
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
OÇ-1
4
4
4
4
4
4
3
3
2
2
OÇ-2
4
3
3
3
4
3
3
3
OÇ-3
5
5
5
5
5
5
3
3
3
3
3
OÇ-4
4
5
4
5
5
5
3
3
3
3
3
3
OÇ-5
4
5
5
5
4
4
3
3
3
3
3
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026