YAŞAR UNIVERSITY
FACULTY OF ENGINEERING
SOFTWARE ENGINEERING DEPARTMENT
COURSE SYLLABUS
Course Title
Soft Computing
Course
Code
Semester
SENG368
Spring
Course Hour/Week
Theory
3
Practice
0
Yaşar Credit
ECTS
3
5
Course Type
1. Compulsory Courses
1.1. Programme Compulsory Courses
1.2. University Compulsory Courses (UFND)
1.3. YÖK (Higher Education Council) Compulsory Courses
2. Elective Courses
2.1. Program Elective Courses
X
2.2. University Elective Courses
3. Prerequisites Courses
3.1. Compulsory Prerequisites Courses
3.2. Elective Prerequisites Courses
Language of Instruction
English
Level of Course
Undergraduate Degree (First Cycle)
Prerequisites Course(s) (compulsory)
N/A
Special Pre-Conditions of the Course
(recommended)
N/A
Course Coordinator
Assist. Prof. Dr. Mete Eminağaoğlu
Mail: mete.eminagaoglu@yasar.edu.tr
Web:
Course Instructor(s)
Assist. Prof. Dr. Mete Eminağaoğlu
Mail: mete.eminagaoglu@yasar.edu.tr
Web:
Course Assistant(s)/Tutor (s)
-
Mail: Web:-
Aim(s) of the Course
To develop skills, knowledge and basic experience for all of the computer and
software engineering students in order to design, implement and use several artificial
intelligence methodologies, techniques, algorithms and related systems (such as
machine learning, fuzzy logic, data mining, data analysis, evolutionary computation,
optimization) in all kinds of computer and software projects such as senior projects,
research projects, other IT projects and in their business life.
1. Ability to develop construction of new data processing models and software
projects with simplicity, robustness and low-cost.
2. Gain knowledge about methodologies to avoid demand of over-precision in
computations, expert systems, decision support systems, problem-solving and
software applications.
Learning Outcomes of the Course
3. Gain skills to implement several implementations, methods and models of artificial
intelligence in software projects.
4. Gain knowledge and skills to analyze, prepare, cleanse, evaluate and validate
mission-critical data in business applications.
5. Ability to derive solutions for complex linear and/or non-linear business and
industrial problems by the aid of fuzzy logic, optimization algorithms, machine
learning predictions and classifications.
Course Content
NP-hard and NP-complete problems. Optimization techniques and methodologies,
linear and non-linear models, genetic algorithms, evolutionary computational models,
multi-valued logic and predicate logic, fuzzy logic and fuzzy theory, implementation of
fuzzy logic models, data mining and machine learning, data preprocessing,
exploratory data analysis, prediction and estimation, classification, Bayesian
Probability, Bayesian classifiers and estimators, neural networks, clustering,
association rules, model evaluation techniques.
COURSE OUTLINE/SCHEDULE (Weekly)
Week
Preliminary Preparation
Methodology and
Implementation
(theory, practice,
assignment, etc.)
-
theory
Topics
1
Introduction to soft computing; basic concepts of
artificial intelligence, decision support systems,
expert systems, uncertainty and game theory in
business and real life. NP-complete and NP-hard
problems.
2
Propositional logic and predicate logic, Boolean logic, Students must have done the
multi-valued logic. Basic models and examples of 3exercises given in the previous
valued logic.
course
theory
3
Introduction to fuzzy logic concepts. Crisp set and
fuzzy set. Basic concepts of fuzzy sets, membership
functions. Basic operations on fuzzy sets, Properties
of fuzzy sets, fuzzy relations.
theory
4
Formulation and parameterization of fuzzy rules and
Students must have done the
fuzzy reasoning. Fuzzy relations and fuzzy if-then
exercises given in the previous
rules. Fuzzy functions and their applications by simple
course
examples.
theory , practice
5
Basic concepts in data mining and machine learning.
Students must have done the
Binary and multi-classification, numerical prediction,
exercises given in the previous
clustering and association algorithms. Supervised and
course
unsupervised learning.
theory
6
Data analysis, preparation and cleansing in machine
learning. Different evaluation methods in machine
learning. Over-fitting, cross-validation and ROC
curves.
Students must have done the
exercises given in the previous
course
theory , practice
7
Bayesian probability. Naïve Bayes, Bayesian networks Students must have done the
and other Bayesian classifiers and some basic
exercises given in the previous
numerical prediction methods in Bayesian learning.
course
theory , practice
8
Midterm Exam
Students must study before the
exam
-
9
Basic concepts in artificial neural networks. Single
layer perceptron and multi-layer perceptron models.
Back propagation neural networks.
Students must have done the
exercises given in the previous
course
theory
10
Applications and implementations of several neural
network algorithms and their evaluation methods
with examples. Support vector machines with
implementations and examples.
Students must have done the
exercises given in the previous
course
theory , practice
11
Decision trees and several decision tree algorithms
and their implementations for classification and
numerical prediction.
Students must have done the
exercises given in the previous
course
theory , practice
12
Introduction to genetic algorithms. Survival of the
fittest, fitness computations, crossover, mutation,
reproduction. Rank method-rank space method.
Students must have done the
exercises given in the previous
course
theory
Students must have done the
exercises given in the previous
course
13
Random search, greedy search and downhill search
Students must have done the
methods and algorithms. Implementations of genetic
exercises given in the previous
algorithms for optimization concepts and problems.
course
Encoding and fitness concepts in genetic algorithms.
theory , practice
14
Other evolutionary soft computing methodologies
and their implementations– Part 1: Ant colony
optimization
Students must have done the
exercises given in the previous
course
theory
15
Other evolutionary soft computing methodologies
and their implementations– Part 2: Artificial bee
colony and swarm intelligence
Students must have done the
exercises given in the previous
course
theory
16
Final Exam
Students must study before the
exam
-
Required Course Material (s) /Reading(s)/Text
Book (s)
Eva Volna, Introduction to Soft Computing, ISBN-13: 9788740303919,
Bookboon, 2013.
S. N. Sivanandam & S. N. Deepa, Principles of Soft Computing, Wiley
India Pvt. Limited, 2007.
Recommended Course Material
(s)/Reading(s)/Other
Daniel T. Larose, Discovering Knowledge in Data: An Introduction to
Data Mining, John Wiley & Sons, Inc., 2005.
Ian H. Witten , Eibe Frank , Mark A. Hall, Data Mining: Practical
Machine Learning Tools and Techniques, 3rd Edition, The Morgan
Kaufmann Series, 2011.
Lotfi A. Zadeh & Ronald R. Yager, An Introduction to Fuzzy Logic
Applications in Intelligent Systems, Springer, 2012.
ASSESSMENT
Semester Activities/ Studies
NUMBER
WEIGHT in %
1
40
Quiz
3
30
Assignment (s)
2
30
Mid- Term
Attendance
Project
Laboratory
Field Studies (Technical Visits)
Presentation/ Seminar
Practice (Laboratory, Virtual Court, Studio Studies etc.)
Other (Placement/Internship etc.)
TOTAL
100
Contribution of Semester Activities/Studies to the Final Grade
60
Contribution of Final Examination/Final Project/ Dissertation to the Final Grade
40
TOTAL
.
100
CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME OUTCOMES
No Programme Outcomes
Level of
Contribution (1lowest/ 5highest)
1
1
2
3
4
To identify, formulate, and solve software engineering problems by applying knowledge of
mathematics, science and engineering


2 To design and conduct scientific and engineering experiments and to analyze and interpret data
3 To identify, formulate and validate user needs and system requirements
5

4
To develop large and complex software systems as applying software engineering principles and
techniques


5 To use modern engineering techniques for analysis and design

6 To recognize the importance of abstraction and modeling
7
To use efficient software engineering background for being able to follow up most recent
developments in the field and other segments of life by utilizing lifelong learning principles

To collect data regarding special domain knowledge in other fields and disciplines beyond the
8 computing discipline for the purposes of supporting software development in specific domains of
application
9
To demonstrate efficient communication skills in written and oral forms by making use of
language competences and effectively work both individually and as a team member



10 To adopt the ethical and professional responsibility and use them in professional life
ECTS /STUDENT WORKLOAD
ACTIVITIES
NUMBER
UNIT
HOUR
TOTAL
(WORKLOAD)
14
Week
3
42
2
28
Number
8
16
Course Teaching Hour (14 weeks* total course hours)
Preliminary Preparation and finalizing of course notes,
further self- study
14
Assignment (s)
2
Week
Presentation/ Seminars
-
-
-
Quiz and Preparation for the Quiz
3
Number
3
9
Mid- Term(s)
1
Number
10
10
Project (s)
-
-
-
Field Studies (Technical Visits, Investigate Visit etc.)
-
-
-
Practice (Laboratory, Virtual Court, Studio Studies etc.)
-
-
Final Examination/ Final Project/ Dissertation and
Preparation
1
Number
Other (Placement/Internship etc.)
-
12
-
12
-
Total Workload
117
Total Workload/ 25
4.68
ECTS
5
ETHICAL RULES WITH REGARD TO THE COURSE (IF AVAILABLE)
None.
ASSESSMENT and EVALUATION METHODS:
Final Grades and assessment criteria are determined according to the Yaşar University Associate Degree, Bachelor
Degree and Graduate Degree Education and Examination Regulation.
PREPARED BY
Assist. Prof. Dr. Mete Eminağaoğlu
UPDATED
03.05.2014; 22:00
APPROVED