1	Course Title:	ARTIFICIAL INTELLIGENCE
2	Course Code:	END6122
3	Type of Course:	Optional
4	Level of Course:	Third Cycle
5	Year of Study:	1
6	Semester:	2
7	ECTS Credits Allocated:	7,5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. NURSEL ÖZTÜRK
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	nursel@uludag.edu.tr +90 224 2942083 Uludağ Üniversitesi, Endüstri Mühendisliği Bölümü
18	Website:
19	Objective of the Course:	The objective of this course is to provide students the knowledge of Artificial Intelligence and related topics with engineering applications.
20	Contribution of the Course to Professional Development

21

Learning Outcomes:

1 Will be able to understand knowledge of the artificial intelligence and related topics ; 2 Will be able to design an intelligent system with using expert system, fuzzy logic, neural network, etc.; 3 Will be able to present an artificial intelligence project;

22	Course Content:

Week	Theoretical	Practical
1	Fundamental principles of artificial intelligence
2	Expert System, Knowledge Engineering, General structure of expert system
3	Knowledge representation techniques, Search techniques, Inference
4	Design of expert systems, Forward chaining, Backward chaining
5	Probability and expert systems, Application examples, Presentation of homework 1
6	Fuzzy sets, Properties of fuzzy sets, Fuzzy set operations
7	Fuzzy relations, Membership functions, Fuzzification
8	Inference techniques, Defuzzification techniques
9	Natural language, Fuzzy systems,
10	Fuzzy systems, Application examples, Presentation of homework 2
11	Midterm Exam, Artificial neural networks
12	Artificial neural networks
13	Artificial neural networks, Application examples, Presentation of homework 3
14	Oral presentation of projects

23	Textbooks, References and/or Other Materials:	N. Öztürk, “Artificial Intelligence Lecture Notes”. P.H. Winston, “Artificial Intelligence”. K. Parsaye, M. Chignell, “Expert Systems for Experts”. T.J. Ross, “Fuzzy Logic With Engineering Applications”. L.H. Tsoukalas, R.E. Uhrig, “Fuzzy and Neural Approaches in Engineering”. S. Haykin, “Neural Networks”. Articles
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	20
Quiz	0	0
Homeworks, Performances	4	50
Final Exam	1	30
Total	6	100
Contribution of Term (Year) Learning Activities to Success Grade		70
Contribution of Final Exam to Success Grade		30
Total		100
Measurement and Evaluation Techniques Used in the Course
Information

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ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	14	10	140
Homeworks, Performances	4	3	12
Projects	1	25	25
Field Studies	0	0	0
Midtermexams	1	2,5	2,5
Others	0	0	0
Final Exams	1	3,5	3,5
Total WorkLoad			225
Total workload/ 30 hr			7,5
ECTS Credit of the Course			7,5

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 0 0 5 0 0 0 0 0 5 0 0 5 LO2 0 0 5 4 5 0 0 0 5 0 0 5 LO3 0 0 0 0 0 5 5 5 0 0 4 5

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High