CMP5133 Artificial Neural NetworksBahçeşehir UniversityDegree Programs ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)
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 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
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
Recommended Optional Program Components: None
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 Outcomes

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

Sources

Course Notes / Textbooks: 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
Homework Assignments 5 % 25
Presentation 1 % 10
Project 1 % 25
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 35
PERCENTAGE OF FINAL WORK % 65
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Project 13 65
Homework Assignments 13 65
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