Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP5133 Artificial Neural Networks Fall 3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Course Lecturer(s): Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Course Objectives: The objective of this course is to introduce the fundamental artificial neural network architectures and algorithms. Students will also learn to use neural networks in order to solve real world problems.

Learning Outputs

The students who have succeeded in this course;
I. Explain the learning and generalization aspects of neural network systems.
II. Be able to apply backpropagation algorithm to a classification problem
III. Be able to apply support vector machines to a classification problem.
IV. Be able to implement self organizing maps.
V. Describe and explain the most common architectures and learning algorithms

Course Content

Perceptrons, linear regression, least mean squares algorithm, multi-layer perceptrons, backpropagation algorithm, support vector machines, radial basis function networks, self organizing maps, recurrent neural networks.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction
2) Perceptron
3) Linear regression
4) Least mean squares algorithm.
5) Multi-layer preceptrons.
6) Backpropagation algorithm.
7) Support vector machines
8) Support vector machines
9) Radial basis function networks.
10) Radial basis function networks
11) Self organizing maps
12) Self organizing maps
13) Recurrent neural networks
14) Recurrent neural networks


Course Notes: Neural Networks and Learning Machines By Simon Haykin Publisher: Prentice Hall; 3 edition
References: Yok - None

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments 5 % 25
Presentation 1 % 10
Project 1 % 25
Seminar % 0
Midterms % 0
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class
Presentations / Seminar
Project 13 65
Homework Assignments 13 65
Preliminary Jury
Paper Submission
Final 5 19
Total Workload 191

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) Understand and implement advanced concepts of Siber Security
2) Use math, science, and modern engineering tools to formulate and solve advenced siber security problems.
3) Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
4) Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
5) Work effectively in multi-disciplinary research teams
6) Acquire scientific knowledge
7) Find out new methods to improve his/her knowledge.
8) Effectively express his/her research ideas and findings both orally and in writing
9) Defend research outcomes at seminars and conferences.
10) Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc.
11) Demonstrate professional and ethical responsibility.