EXPERT SYSTEMS PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
BIL419	EXPERT SYSTEMS	5	3	3	6

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course :	Elective
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit	Assist.Prof. SERKAN GÖNEN
Instructor(s) of the Course Unit	Assist.Prof. GÖKAY BURAK AKKUŞ
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The aim of this course is to provide an understanding of the fundamentals of expert systems and their components, as expert systems play a significant role among real-world artificial intelligence applications.
Contents of the Course Unit:	This course covers fundamental concepts, inference engines, knowledge bases, knowledge acquisition, representation and control of knowledge, automated reasoning, uncertainty representation, practical problem-solving, the development of expert systems in theory and practice, well-known examples of expert systems, and software tools and architectures for expert system design.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Applying the necessary methodology to transfer human knowledge to an expert system, knowledge representation, and knowledge base design
Designing a rule-based expert system and evaluating expert system tools
Using artificial intelligence languages for the design of an expert system

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	-	Introduction	-
2	-	Introduction to Artificial Intelligence	-
3	-	What is an Expert System?	-
4	-	Structure and Basic Elements of Artificial Neural Networks	-
5	-	Knowledge Engineering	-
6	-	Methods of Knowledge Representation	-
7	-	Fundamental Structure of Expert Systems	-
8	-	MID-TERM EXAM	-
9	-	Expert System Design Methods	-
10	-	Designing Expert Systems Using Probability Theory, Fuzzy Logic, and Neural Network Methods	-
11	-	Examples of Expert System Design	-
12	-	Expert System Applications	-
13	-	Expert System Applications	-
14	-	Tools and Shells	-
15	-	Student Project Presentations	-
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Introduction to Expert System. By Peter Jackson, AddisonWesley Publishing Company, ISBN 0-201-17578-9, 1990-2
An introduction to Expert System. By Michell Gondron, McGraw Hill
Introduction to Expert System. By James P. Egnizo
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114
Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition
Uzman Sistemler, Prof.Dr. Novru ALLAHVERDİ, Atlas Yayın Dağıtım, 2002

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	30
Practice	1	20
Final Exam	1	50
TOTAL	3	100

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Ability to identify, analyze, design, model and solve complex engineering problems based on engineering, science and mathematics fundamentals

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Ability to apply engineering design to produce solutions that meet specific needs, taking into account global, cultural, social, environmental and economic factors as well as public health, safety and well-being

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Ability to communicate effectively with various stakeholders

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The ability to recognize ethical and professional responsibilities in engineering and make informed decisions considering the impact of engineering solutions in their global, economic, environmental and social contexts

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The ability to recognize ethical and professional responsibilities in engineering and make informed decisions considering the impact of engineering solutions in their global, economic, environmental and social contexts

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Ability to acquire new knowledge and find ways to apply it when necessary, using appropriate learning strategies

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Ability to work effectively in a team where its members lead together, create a collaborative and inclusive environment, set goals, plan tasks, and meet goals

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Ability to design and conduct appropriate experiments, analyze and interpret data, and apply engineering principles to draw conclusions

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	14	1	14
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	2	2
Preparation for the Final Exam	1	35	35
Mid-Term Exam	1	2	2
Preparation for the Mid-Term Exam	1	30	30
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	32	0	125