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 4123 SOFT COMPUTING TECHNIQUES ELECTIVE 2 2 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR AYŞE ÖVGÜ KINAY

Offered to

Computer Science

Course Objective

The aim of this course is to introduce students to soft computing techniques and methods and to give them the ability to solve complex real-world problems that are difficult to solve using exact mathematical methods. In this context, simulated annealing, genetics and various swarm intelligence algorithms are examined.

Learning Outcomes of the Course Unit

1   Have a basic knowledge of soft computing techniques.
2   Have a basic knowledge of genetic algortihms.
3   Have a basic knowledge of evolutionary algorithms.
4   Have a basic knowledge of swarm intelligence algorithms.
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 techniques
2 Simulated annealing algorithm
3 Genetic algorithm
4 Genetic algorithm (continue)
5 Evolutionary computation
6 Genetic programming
7 Article reviews - Project presentation
8 Review of topics
9 Swarm Intelligence
10 Partical Swarm Intelligence
11 Ant colony optimization
12 Ant colony optimization (cont)
13 Artificial bee colony optimization
14 Project presentation

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 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.20 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.20 + RST * 0.40


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)

ovgu.tekin@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Preparing assignments 2 10 20
Final 1 2 2
Midterm 1 1 1
Project Assignment 2 2 4
TOTAL WORKLOAD (hours) 136

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