Natural Computing (CMPE564) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Natural Computing CMPE564 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Computer Engineering Elective Courses
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The objective of this course is to teach different nature inspired computing techniques; to gain an insight about how to solve real-life practical computing and optimization problems.
Course Learning Outcomes The students who succeeded in this course;
  • Gain necessary knowledge about nature-inspired computing mechanisms, including Hill Climbing, Simulated Annealing, Genetic Algorithms, Neural Networks, Swarm Intelligence (e.g. Ant Colonies, Particle Swarm Optimization) and Artificial Immune Systems.
  • Understand and improve the mentioned nature inspired computing techniques
  • Applying the nature-inspired computing techniques to real-life practical problems
  • Develop necessary software codes in the nature-inspired computing context.
Course Content Problem solving by search, hill climbing, simulated annealing, artificial neural networks, genetic algorithms, swarm intelligence (including ant colony optimization and particle swarm optimization), artificial immune systems.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to Natural Computing Chapter 1 & 2 (Course Book)
2 Introduction to Natural Computing Chapter 1 & 2 (Course Book)
3 Problem Solving by Search; Hill Climbing; Simulated Annealing Chapter 3 (Course Book) and Source #1
4 Evolutionary Computing: Genetic Algorithms. Chapter 3 (Course Book) and Source #1
5 Evolutionary Computing: Genetic Algorithms. Chapter 3 (Course Book) and Source #1
6 Neurocomputing and Artificial Neural Networks Chapter 4 (Course Book) and Source #2
7 Neurocomputing and Artificial Neural Networks Chapter 4 (Course Book) and Source #2
8 Swarm Intelligence: Ant Colony Optimization Chapter 5 (Course Book) and Source #3
9 Swarm Intelligence: Ant Colony Optimization Chapter 5 (Course Book) and Source #3 Chapter 5 (Course Book)
10 Swarm Intelligence: Particle Swarm Optimization Chapter 5 (Course Book)
11 Swarm Intelligence: Particle Swarm Optimization Chapter 5 (Course Book)
12 Artificial Immune Systems Chapter 6 (Course Book)
13 Artificial Immune Systems Chapter 6 (Course Book)
14 Artificial Immune Systems Chapter 6 (Course Book)
15 Review
16 Review

Sources

Course Book 1. Leandro Nunes de Castro, Fundamentals of Natural Computing: Basic Concepts, Algorithms and Applications, Chapman & Hall/CRC, 2006, ISBN 1-58488-643-9.
Other Sources 2. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2003, ISBN: 0-13-790395-2.
3. J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, 1991, ISBN: 0-201-50395-6.
4. M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004. ISBN: 0-262-04219-3.
5. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 2 20
Presentation 1 20
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 40
Toplam 5 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Comprehends the most advanced technology and literature in the field of software engineering research. X
2 Gains the ability to conduct world-class research in software engineering and publish scholarly articles in top conferences and journals in the area.
3 Conducts quantitative and qualitative studies in software engineering. X
4 Develops and applies software engineering approaches to acquire the necessary skills to bridge the gap between academia and industry in the field of software engineering and to solve real-world problems. X
5 Gains the ability to access the necessary information to follow current developments in science and technology, and to conduct scientific research or develop projects in the field of software engineering. X
6 Gains awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of software engineering.
7 Acquires project and risk management skills; gains awareness of the importance of entrepreneurship, innovation, and sustainable development; adapts international excellence standards for software engineering practices and methodologies.
8 Gains awareness of the universal, environmental, social, and legal consequences of software engineering practices when making decisions.
9 Develops, adopts, and supports the sustainable use of excellence standards for software engineering practices.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration 1 5 5
Project
Report
Homework Assignments 2 5 10
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 131