Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
CSE 5013	NEURAL NETWORKS	ELECTIVE	3	0	0	9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR DERYA BIRANT

Offered to

Computer Engineering Non-Thesis
COMPUTER ENGINEERING
Computer Engineering
Computer Engineering
Computer Engineering (Non-Thesis-Evening)

Course Objective

The main objective of this course is to present various neural networks (Multilayer Perceptrons, Radial Basis Function Networks, Self Organizing Maps, etc.) and to apply them in the solution of engineering problems.

Learning Outcomes of the Course Unit

1	Describe the relation between real brains and simple artificial neural network models
2	Have detailed knowledge about various artificial neural networks
3	Analyze the performance of artificial neural networks
4	Understand the differences among various network types
5	Apply artificial neural networks in practical problems

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to Neural Networks and their History. Biological Neurons and Neural Networks. Artificial Neurons
2	Networks of Artificial Neurons. Single Layer Perceptrons. Learning and Generalization in Single Layer Perceptrons
3	Hebbian Learning. Gradient Descent Learning
4	The Generalized Delta Rule. Practical Considerations
5	Learning in Multi-Layer Perceptrons. Back-Propagation
6	Learning with Momentum. Conjugate Gradient Learning
7	Bias and Variance. Under-Fitting and Over-Fitting.
8	Deep Learning
9	Applications of Multi-Layer Perceptrons
10	Radial Basis Function Networks: Introduction
11	Radial Basis Function Networks: Algorithms, Applications Comittee MAchines
12	Self Organizing Maps: Fundamentals, Algorithms and Applications
13	Learning Vector Quantization (LVQ)
14	Project Presentations

Recomended or Required Reading

Textbook(s):
D. Graupe, Principles of Artificial Neural Networks, WSPC, 2013
C.C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer, 2018

Supplementary Book(s):
S. Haykin, Neural Networks And Learning Machines, Pearson India, 2018
M.T. Hagan, H.B. Demuth, M.H. Beale, O. De Jesús, Neural Network Design, Martin Hagan, 2014

Planned Learning Activities and Teaching Methods

Lectures
Literature Review / Research
Presentation
Term Project
Report

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	ASG	ASSIGNMENT
2	PRS	PRESENTATION
3	FCG	FINAL COURSE GRADE	ASG * 0.50 + PRS * 0.50

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic, literature review, and project / report prepared by the student.

Language of Instruction

English

Course Policies and Rules

Code writing knowledge and skills are required.
Participation is mandatory.

Contact Details for the Lecturer(s)

Assoc.Prof.Dr. Derya BIRANT
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35390 Buca / IZMIR
Tel: +90 (232) 301 74 01
E-mail: derya@cs.deu.edu.tr

Office Hours

Tuesday 9:30 - 10:30

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	14	3	42
Preparations before/after weekly lectures	14	2	28
Web Search and Library Research	1	5	5
Reading	10	3	30
Project Preparation	1	65	65
Preparing presentations	1	30	30
Preparing report	1	25	25
TOTAL WORKLOAD (hours)			225

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.2	PO.3	PO.4	PO.5	PO.7	PO.8	PO.9	PO.10	PO.11
LO.1	4
LO.2	4	4					4
LO.3		5					4		3	4
LO.4			4		4	4
LO.5				5		5		4

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION