PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ISY 608	ARTIFICIAL NEURAL NETWORKS	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Doctorate Degree
Course Type	Elective
Course Objective	Developed high performance, flexible and has one of these methods, this method of artificial neural networks for a solution to the problems that are difficult due to the uncertainty, the convenience of a different approach in order to: Neural fuzzy logic Networks, Fuzzy rules and membership functions such as the point of the scientific concepts of alternative approach thoughts
Course Content	Introduction to Neural Networks, common items of artificial neural networks, establishment of artificial neural networks perceptron, delta rule, feed forward networks, feedback networks, structure of artificial neural networks, learning in artificial neural networks, counseling non-learning, mixed learning rule, learning Competitors , competitors nervous signs, map network, artificial neural network applications. .
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Learns the concept of artificial nerve
2	Analatik the thought of ability to comprehend
3	Enables Optimization problems by using artificial neural networks
4	classifies the methods of learning
5	Uncertainty environment offers a different approach

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07
LO 001	4	5	2	5			4
LO 002	5	4	2	5			4
LO 003	5	4	2	5			5
LO 004	4	4	1	5			5
LO 005	5	4	1	5			5
Sub Total	23	21	8	25			23
Contribution	5	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	4	56
Assignments	5	5	25
Mid-terms	1	20	20
Final examination	1	22	22
Presentation / Seminar Preparation	3	10	30
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ISY 608	ARTIFICIAL NEURAL NETWORKS	3 + 0	1	Turkish	2023-2024 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. İRFAN ERTUĞRUL	iertugrul@pau.edu.tr		İİBF A0135	%77

Goals		Developed high performance, flexible and has one of these methods, this method of artificial neural networks for a solution to the problems that are difficult due to the uncertainty, the convenience of a different approach in order to: Neural fuzzy logic Networks, Fuzzy rules and membership functions such as the point of the scientific concepts of alternative approach thoughts
Content		Introduction to Neural Networks, common items of artificial neural networks, establishment of artificial neural networks perceptron, delta rule, feed forward networks, feedback networks, structure of artificial neural networks, learning in artificial neural networks, counseling non-learning, mixed learning rule, learning Competitors , competitors nervous signs, map network, artificial neural network applications. .

Topics

Weeks	Topics
1	fuzzy logic
2	intrduction to neural networks
3	introduction to neural networks
4	establishment of artificial neural networks
5	establishment of artificial neural networks
6	establishment of artificial neural networks
7	establishment of artificial neural networks
8	visa
9	learning
10
11
12
13
14

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