1	Course Title:	ARTIFICIAL INTELLIGENCE
2	Course Code:	IYS4216
3	Type of Course:	Optional
4	Level of Course:	First Cycle
5	Year of Study:	4
6	Semester:	8
7	ECTS Credits Allocated:	6
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. MELİH ENGİN
16	Course Lecturers:	Doç.Dr. Melih ENGİN
17	Contactinformation of the Course Coordinator:	Doç.Dr. Melih ENGİN 0224 294 26 95 melihengin@uludag.edu.tr
18	Website:
19	Objective of the Course:	Giving basic definitions and concepts about artificial intelligence, understanding fuzzy expert systems and applications.
20	Contribution of the Course to Professional Development	To be able to design the systems necessary for an enterprise and to produce solutions for the needs of iders.

21

Learning Outcomes:

1 Recognize scientific intelligence methods, science, information and informatics, flexible methods and types; 2 Understands artificial neural networks and their basic properties; 3 Understands the structure and basic features of expert systems; 4 Understands the genetic algorithm and its basic properties; 5 Understands the ai and its basic properties;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to Artificial Neural networks
2	Creation of artificial neural networks
3	Structures of Artificial Neural Networks
4	Consultancy and non-consultancy learning
5	Artificial Neural Networks Applications
6	Fuzzy Logic
7	Fuzzy Logic
8	Fuzzy Logic Controller systems
9	Fuzzy Logic Controller systems
10	Genetic algorithm
11	Genetic algorithm
12	Genetic algorithm
13	Genetic algorithm
14	Genetic algorithm

23	Textbooks, References and/or Other Materials:	Çetin Elmas, Yapay Zeka Uygulamaları, Seçkin Yayıncılık
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Relative Evaluation
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	0	0	0
Homeworks, Performances	0	0	0
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	60	60
Others	0	0	0
Final Exams	1	75	75
Total WorkLoad			237
Total workload/ 30 hr			5,9
ECTS Credit of the Course			6

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 LO1 3 4 4 5 5 0 5 5 0 0 4 LO2 3 4 4 5 5 0 5 5 0 0 4 LO3 3 4 4 5 5 0 5 5 0 0 4 LO4 3 4 4 5 5 0 5 5 0 0 4 LO5 3 4 4 5 5 0 5 5 0 0 4

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High