Expert Systems

Course Code:

COMP 402

Course Type:

Area Elective

P:

3

Lab:

0

Credits:

3

ECTS:

6

Course Language:

İngilizce

Course Objectives:

In this course, students will learn the concept of expert systems and, how to design an expert system.

Course Content:

Basic concepts; Inference engine; Knowledge base; Knowledge elicitation; Representation and control of knowledge; Automated reasoning; Representing uncertainty; Practical problem solving; Development of the theory and practice of expert systems; Well known samples of expert systems; Software tools and architectures for building expert systems

Course Methodology:

1: Anlatım, 2: Soru-Cevap, 3: Tartışma 4: Uygulama

Course Evaluation Methods:

A: Sınav B: Laboratuar C: Ödev D: Proje

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
Implements an expert system	11	1, 2, 3	A,C
Determines inference mechanism for a given problem	7	1, 2, 3	A,C
Determines knowledge representation method for a given problem	7	1,3,5	A,C,E
Knows the commonsense databases and their construction phases	8	1, 2, 3,5	A,C,E
To compare the design patterns and explain which design pattern should be used.	3	1, 2, 3	A,E

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction, History	Lecture Notes
2	Basic concept: inference engine	Lecture Notes
3	Knowledge base	Lecture Notes
4	Knowledge elicitation	Lecture Notes
5	Representation and control of knowledge	Lecture Notes
6	Automated reasoning	Lecture Notes
7	Representing uncertainty	Lecture Notes
8	Mid-term Exam
9	Practical problem solving	Lecture Notes
10	Development of the theory and practice of expert systems	Lecture Notes
11	Software tools and architectures for building expert systems	Lecture Notes
12	Implemention of an expert system	Lecture Notes
13	Well known samples of expert systems-1	Lecture Notes
14	Well known samples of expert systems-2	Lecture Notes
15	Final Exam

Recommended Sources

RECOMMENDED SOURCES
Textbook	Introduction to Expert Systems, Jackson P. , 3rd edition, Addison Wesley, ISBN 0-201-87686-8 Giarratano J. , Riley G. , Expert Systems, Principles and Programming, PWS Publising Company, Boston., ISBN 0-534-93744-6
Additional Resources	Introduction to Knowledge Systems, Stefik M., Morgan Kaufmann, ISBN 1-55860-166-X

Material Sharing

MATERIAL SHARING
Documents	Guidelines and additional examples for Lecture Topics
Assignments	Homework Assignments
Exams	Midterm Exam and Final Exam

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	2	2 X 40
LAB AND Quizzes	-	20
Attendance	-	0
Total		100
Contribution of Final Examination to Overall Grade		50
Contribution of In-Term Studies to Overall Grade		50
Total		100

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 15x Total course hours)	15	3	45
Hours for off-the-classroom study (Pre-study, practice)	15	4	60
Mid-terms	2	10	20
Homework	14	1	14
Final examination	1	10	10
Total Work Load			149
Total Work Load / 25 (h)			5,96
ECTS Credit of the Course			6

None

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