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
STA 6016	SOFT COMPUTING TECHNOLOGIES	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR EFENDI NASIBOĞLU

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Science
Industrial Ph.D. Program In Advanced Biomedical Technologies
Biomedical Tehnologies (English)
Statistics
Statistics
Ph.D. in Computer Science
STATISTICS

Course Objective

The objective of the course is to introduce basics of Soft Computing (SC) technologies which has gains popularity in data analysis and decision making problems for recent years.

Learning Outcomes of the Course Unit

1	Have a good understanding of basic SC concepts.
2	Have a good understanding about the SC techniques.
3	Have a basic knowledge of current SC applications.
4	Have ability to use a basic SC software.
5	Have ability to design SC applications.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	INTRODUCTION TO FUZZY AND SOFT COMPUTING
2	FUZZY SETS
3	FUZZY RULES AND FUZZY REASONING
4	FUZZY INFERENCE SYSTEMS, ASSIGNMENT 1
5	LEAST-SQUARES METHODS FOR SYSTEM IDENTIFICATION
6	DERIVATIVE-BASED OPTIMIZATION, ASSIGNMENT 2
7	DERIVATIVE-FREE OPTIMIZATION
8	Midterm exam
9	NEURAL NETWORKS
10	ANFIS: ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS
11	CLASSIFICATION AND REGRESSION TREES
12	DATA CLUSTERING ALGORITHMS, ASSIGMENT 3
13	RULEBASE STRUCTURE IDENTIFICATION
14	SOFT COMPUTING APPLICATIONS

Recomended or Required Reading

Textbook(s)/References/Materials:
J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy And Soft Computing, by Prentice Hall, 1997.
Supplementary Book(s):
R.A. Aliev, R.R. Aliev, Soft Computing and Its Applications, by World Scientific Publishing Co., 2001.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	PRJ	PROJECT
2	FCG	FINAL COURSE GRADE	PRJ * 1

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

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homeworks.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

efendi.nasibov@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	13	4	52
Preparation before/after weekly lectures	13	4	52
Preparing Individual Assignments	3	12	36
Preparation for Mid-term Exam	1	26	26
Preparation for Final Exam	1	30	30
Final	1	2	2
Mid-term	1	2	2
TOTAL WORKLOAD (hours)			200

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.4	PO.5	PO.6	PO.7	PO.8
LO.1	3	5	5	5	5	5
LO.2	5	5	4	4	3	5
LO.3	5	3	4	5	5	3
LO.4	5	4	5	5	5	5
LO.5	4	5	5	3	5	5

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