COURSE UNIT TITLE

: SOFT COMPUTING TECHNIQUES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 4001 SOFT COMPUTING TECHNIQUES COMPULSORY 4 0 0 7

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR EFENDI NASIBOĞLU

Offered to

Computer Science

Course Objective

This course aims to learn: introduction to soft computing, fundamentals of artificial neural network, fuzzy inference systems, genetic algorithm, simulated annealing, and hybrid systems.

Learning Outcomes of the Course Unit

1   Have a basic knowledge of artificial neural networks.
2   Have a basic knowledge of fuzzy inference systems.
3   Have a basic knowledge of evolutionary algorithms.
4   Have a basic knowledge of hybrid systems.
5   Have ability to construct models with soft computing.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to soft computing
2 Artificial neural network
3 Artificial neural network (continue)
4 Genetic algorithm
5 Genetic algorithm (continue)
6 Random search algorithm
7 Simulated annealing algorithm
8 Midterm exam
9 Fuzzy logic based systems
10 Fuzzy logic based systems (continue)
11 Fuzzy clustering
12 Fuzzy clustering (continue)
13 Hybrid systems
14 Hybrid systems (continue )

Recomended or Required Reading

Textbook(s):
Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications", by S. Rajasekaran and G.A. Vijayalaksmi Pai, (2005), Prentice Hall,
Soft Computing and Intelligent Systems - Theory and Application , by Naresh K. Sinha and Madan M. Gupta (2000), Academic Press,
Supplementary Book(s):
Soft Computing and Intelligent Systems Design - Theory, Tools and Applications", by Fakhreddine karray and Clarence de Silva (2004), Addison Wesley
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence by J. S. R. Jang, C. T. Sun, and E. Mizutani, (1996), Prentice Hall.

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 MTE MIDTERM EXAM
2 QUZ QUIZ
3 ASG ASSIGNMENT
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE * 0.30 + QUZ * 0.10 + ASG * 0.10 + FIN * 0.50
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + QUZ * 0.10 + ASG * 0.10 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

efendi.nasibov@deu.edu.tr
cagin.kandemir@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
Preparations before/after weekly lectures 12 4 48
Preparation for midterm exam 1 14 14
Preparation for final exam 1 20 20
Preparation for quiz etc. 2 3 6
Preparing assignments 2 10 20
Final 1 2 2
Midterm 1 2 2
Quiz etc. 2 0,5 1
TOTAL WORKLOAD (hours) 165

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1545355455
LO.255525455
LO.3554355
LO.455332255
LO.54554345