BIG DATA ANALYTICS AND MANAGEMENT (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 Spring	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 :	Assoc. Prof. CEMAL OKAN ŞAKAR
Course Lecturer(s):	Assoc. Prof. 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. The teaching methods of the course include lectures, individual work, technology-assisted learning, project preparation.

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
1)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.