PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ISY 607	ARTIFICIAL INTELLIGENCE OPTIMIZATION	3 + 0	3rd Semester	10

COURSE DESCRIPTION

Course Level	Doctorate Degree
Course Type	Elective
Course Objective	Many methods are developed for optimization problems, mathematical equations derived from a variety methods.clasic methods based on the apparent lack of flexibility was known as the disadvantages of so many of them artificial intelligence methods have been developed and high performance .variety optimization thanks to this method of identifying problems that are difficult to convenience , for a different approach under uncertainty: Fuzzy Logic, the concept of fuzzy set, fuzzy controllers, Fuzzy Logic Neural Networks, Fuzzy rules and membership functions of a scientific approach to alternative ideas to demonstrate concepts.
Course Content	Introduction to Artificial Intelligence, research areas of artificial intelligence; concepts and techniques of artificial intelligence. Classification of optimization problems and methods, heuristic algorithms, thermal processing algorithm, tabu search algorithm, genetic algorithms, ant colony algorithm, artificial immune algorithm, differential evolution algorithm. application areas of artificial neural networks
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Work on special aspects of artificial intelligence
2	Analaytic understands the ability of thought
3	Allows optimization problems
4	Optmizasyon classifies the methods
5	Offers a different approach to uncertainty

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07
LO 001	4	4	2	5			4
LO 002	5	4	2	5			4
LO 003	4	4	2	5			5
LO 004	4	5	1	5			5
LO 005	5	4	1	5			5
Sub Total	22	21	8	25			23
Contribution	4	4	2	5	0	0	5

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Assignments	5	10	50
Mid-terms	1	25	25
Final examination	1	25	25
Presentation / Seminar Preparation	3	16	48
Total Work Load ECTS Credit of the Course			260 10

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ISY 607	ARTIFICIAL INTELLIGENCE OPTIMIZATION	3 + 0	1	Turkish	2018-2019 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. HÜSEYİN KOÇAK	hkocak@pau.edu.tr		İİBF A0134	%

Goals		Many methods are developed for optimization problems, mathematical equations derived from a variety methods.clasic methods based on the apparent lack of flexibility was known as the disadvantages of so many of them artificial intelligence methods have been developed and high performance .variety optimization thanks to this method of identifying problems that are difficult to convenience , for a different approach under uncertainty: Fuzzy Logic, the concept of fuzzy set, fuzzy controllers, Fuzzy Logic Neural Networks, Fuzzy rules and membership functions of a scientific approach to alternative ideas to demonstrate concepts.
Content		Introduction to Artificial Intelligence, research areas of artificial intelligence; concepts and techniques of artificial intelligence. Classification of optimization problems and methods, heuristic algorithms, thermal processing algorithm, tabu search algorithm, genetic algorithms, ant colony algorithm, artificial immune algorithm, differential evolution algorithm. application areas of artificial neural networks

Topics

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student