Ders bilgi formları arşivlenmiştir ve bilgi amaçlıdır. Güncel ders bilgi formlarına ulaşmak için Akademik Bilgi Sistemi'ni (https://obs.gtu.edu.tr/) ziyaret ediniz.

Course information packages are for archival purposes only. For up-to-date course information packages, please visit Academic Information System (https://obs.gtu.edu.tr/).

• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Basic information
Course title:	Introduction to Learning and Soft Computing
Course code:	ELEC 669
Lecturer:	Assist. Prof. Köksal HOCAOĞLU
ECTS credits:	7.5
GTU credits:	3 (3+0+0)
Year, Semester:	3, Fall and Spring
Level of course:	Second Cycle (Master's)
Type of course:	Area Elective
Language of instruction:	Turkish
Mode of delivery:	Face to face
Pre- and co-requisites:	The ability to program (well) in C or C++ language is essential to complete the computer projects.
Professional practice:	No
Purpose of the course:	The goal of this course is three-folded: (1) to equip students with basic concepts, models, algorithms, and tools for development of intelligent systems. (2) to provide a detailed overview of some advanced topics in computational intelligence (3) to show the student how to use computational intelligence techniques in real settings.

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Implement neural networks and other computational intelligence and machine learning algorithms.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Design and conduct research projects independently

   Method of assessment

   1. Term paper
2. Analyze a given problem, and determine which computational intelligence model to use

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   4. Design and conduct research projects independently
   5. Find out new methods to improve his/her knowledge

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Modify existing algorithms to engineer new algorithms to solve a particular problem at hand.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems

   Method of assessment

   1. Written exam
   2. Homework assignment
4. Validate the computational intelligence techniques presented in more advance texts as well as journal articles.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Acquire scientific knowledge
   4. Design and conduct research projects independently
   5. Effectively express his/her research ideas and findings both orally and in writing
   6. Defend research outcomes at seminars and conferences

   Method of assessment

   1. Term paper

Contents
Week 1:	Introduction to Computational Intelligence topics
Week 2:	Multilayer Neural Networks and Backpropagation
Week 3:	Radial-Basis Function Networks
Week 4:	Recurrent Neural Networks
Week 5:	Fuzzy Set Theory
Week 6:	Fuzzy Relations and Fuzzy Logic Inference
Week 7:	MIDTERM EXAM
Week 8:	Fuzzy Clustering and Classification
Week 9:	Fuzzy Measures and Fuzzy Integrals
Week 10:	Evolutionary Computation: Basic Ideas and Fundamentals
Week 11:	Evolutionary Optimization
Week 12:	Evolutionary Learning and Problem Solving
Week 13:	Collective Intelligence and Other Extensions of Evolutionary Computation
Week 14:	Presentation of term project to class
Week 15*:	General review
Week 16*:	Final exam
Textbooks and materials:	Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation, James M Keller, Derong Liu, and David B. Fogel, Wiley, 2016
Recommended readings:	1. S. Haykin, Neural Networks and Learning Machines, Prentice Hall; 3 edition, 2008 2. A. P. Engelbrecht, Computational Intelligence: An Introduction, John Wiley & Sons, 2007. 3. X. Yu and M. Gen, Introduction to Evolutionary Algorithms, Springer Verlag, 2010. 4. H.K. Lam, S.S.H. Ling, and H.T. Nguyen, Computational Intelligence and Its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine, Imperial College Press, 2011. 5. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer-Verlag; 2nd Edition, 2015 (ISBN 978-3-662-44874-8) 6. J. T. Ross, “Fuzzy Logic With Engineering Applications”, Wiley; 4th Edition, 2016 (ISBN: 978-1-119-23586-6)
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	7	15
Other in-term studies:	4,12	20
Project:	2,9,11	30
Homework:		0
Quiz:	4, 10	10
Final exam:	16	25
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	14
Practice, Recitation:	2	11
Homework:	0	0
Term project:	13	3
Term project presentation:	1	1
Quiz:	0.5	2
Own study for mid-term exam:	12	1
Mid-term:	2	1
Personal studies for final exam:	14	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)