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
EEE 5029	ARTIFICIAL NEURAL NETWORKS	ELECTIVE	3	0	0	7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR YAVUZ ŞENOL

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
Biomedical Tehnologies (English)
ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM) (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

The objectives of the course are:
- to present the basic network architectures and learning rules;
- to provide mathematical methods for neural network analysis and design;
- to provide knowledge for network training and overfitting avoidance;
- to apply neural networks to practical engineering problems in areas of function approximation, pattern recognition, and signal processing.

Learning Outcomes of the Course Unit

1	Describe the relation between real brain and artificial neural network models
2	To be able to design single and multi-layer feed-forward neural networks
3	To be able to explain the differences between supervised and unsupervised learning
4	To be able to explain the behavior or neural networks of the Back-prop, RBF, Hopfield and SOM type
5	To be able to implement ANN algorithms to various engineering problems
6	To be able to analyse the performance of neural networks

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. Biological neurons and neural Networks and their comparison
2	Networks of artificial neurons. Single layer perceptrons.
3	Basic learning rules; generalized LMS, and Hebbian Learning
4	Data processing; Scaling, Transformations, FT, PCA, and wavelets
5	Multi-layer perceptrons and backpropagation.
6	Problem solving with MLP networks and backpropagation learning
7	Learning with momentum and Conjugate Gradient Learning
8	Fitting problems and improving generalization
9	Applications of MLPNN
10	Project Evaluation
11	Radial Basis Function Neural Networks
12	Self Organising Networks and Learning Vector Quantization
13	Recurrent Networks
14	Project evaluation
15	Project evaluation

Recomended or Required Reading

Main Sources: Principles of Neurocomputing for Science & Engineering, McGraw Hill, 2001
Suplementary Sources: Neural Networks A Comprehensive Foundation, Simon Haykın Prentice Hall, 1999
Other Materials: Course notes.

Planned Learning Activities and Teaching Methods

Lecture+Homeworks+Project +Exam

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	ASG	ASSIGNMENT
2	FIN	FINAL EXAM
3	FCG	FINAL COURSE GRADE	ASG * 0.50 + FIN * 0.50
4	RST	RESIT
5	FCGR	FINAL COURSE GRADE (RESIT)	ASG * 0.50 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

yavuz.senol@deu.edu.tr

Office Hours

Posted each term

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	14	3	42
Preparations before/after weekly lectures	14	4	56
Preparation for final exam	1	20	20
Preparing presentations	1	10	10
Preparing assignments	5	5	25
Preparation for project	1	20	20
Final	1	2	2
TOTAL WORKLOAD (hours)			175

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.2	PO.3	PO.4	PO.5	PO.6	PO.7	PO.8	PO.9	PO.10	PO.11	PO.12	PO.13	PO.14
LO.1	1	1
LO.2	2	1							1
LO.3	1	1	1		4	1			1			1
LO.4	2	2	3	1	1	2	1	2	2	1		1	2
LO.5	3	5	3	4	4	2	1	1	2	1	1	1	1	1
LO.6	3	4	3	3	4	1	1	1	2	1	1	1	1

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