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 Bursa 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 applications.
20	Contribution of the Course to Professional Development	The contribution of the course to the professional development is to introduce the knowledge and applications about artificial intelligence, and to provide ability to apply the learned artificial intelligence techniques.

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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, Expert system, General structure of expert system
2	Knowledge representation techniques, Search techniques, Inference, Forward chaining, Backward chaining
3	Design of expert systems, Probability and expert systems, Application examples
4	Fuzzy sets, Properties of fuzzy sets, Fuzzy set operations
5	Fuzzy relations, Membership functions, Fuzzification
6	Fuzzy inference techniques, Defuzzification techniques
7	Natural language, Fuzzy systems
8	Fuzzy systems, Application examples
9	Artificial neural networks
10	Artificial neural networks
11	Artificial neural networks, Application examples
12	Deep learning
13	Deep learning
14	Deep learning, Application examples

23	Textbooks, References and/or Other Materials:	N. Allahverdi, Uzman Sistemler, Bir Yapay Zeka Uygulaması, Atlas Yay. J. C. Giarratano, G.D. Riley, Expert Systems Principles and Programming, Thomson Course Technology. S. N. Sivanandam, S. Sumathi, S. N. Deepa, Introduction to Fuzzy Logic using MATLAB, Springer, 2007. T.J. Ross, Fuzzy Logic with Engineering Applications, Wiley, 2010. A. Yılmaz, Yapay Zeka, Kodlab, 2020. P. Kim, MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, Apress, 2017. Y. Özkan, Uygulamalı Derin Öğrenme: Yapay Zeka, Makine Öğrenmesi, Yapay Sinir Ağları, Papatya Yay. 2021. S. Shanmuganathan, S. Samarasinghe, Artificial Neural Network Modelling, Springer, 2016.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	4	40
Final Exam	1	60
Total	5	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	Homework, Project, Final Exam
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	5	15
Projects	1	25	25
Field Studies	0	0	0
Midtermexams	0	0	0
Others	0	0	0
Final Exams	1	3	3
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 PQ13 PQ14 PQ15 PQ16 LO1 0 0 5 0 0 0 0 0 5 0 0 5 0 0 0 0 LO2 0 0 5 4 5 0 0 0 5 0 0 5 0 0 0 0 LO3 0 0 0 0 0 5 5 5 0 0 4 5 0 0 0 0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High