Department of Biomedical Engineering

MCE 460 | Course Introduction and Application Information

Course Name

Intelligent Systems

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MCE 460	FALL	3	0	3	6

Prerequisites	None
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-to-face
Teaching Methods and Techniques of the Course	Problem Solving Q&A Lecture / Presentation
National Occupational Classification Code	-
Course Coordinator	Dr. Öğr. Üyesi Musa Özgün Güleç
Course Lecturer(s)	Dr. Öğr. Üyesi Musa Özgün Güleç
Assistant(s)	-

Course Objectives

This course will provide Mechatronic engineering students with fundamental knowledge and skills in using intelligent systems. Students will learn how to use neural networks, fuzzy logic, and other nature-inspired algorithms. By examining case studies, they will gain experience in applications to real engineering problems.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Define intelligent systems.				X
LO2	Be able to design multi-layered artificial neural networks.			X
LO3	Be able to use fuzzy logic and neuro-fuzzy systems.			X
LO4	Compare optimization methods based on derivatives with optimization methods inspired by nature.			X
LO5	Will be able to suggest solutions to different problems with intelligent methods.					X

Course Description

Introduction to intelligent systems and nature inspired algorithms. Review for Optimization, modeling and control. Introduction to neural networks, back propagation learning rule, fuzzy set theory, fuzzy inference methods, fuzzy control, adaptive neuro-fuzzy inferencing system (ANFIS), genetic algorithms. Case studies with applications.

Related Sustainable Development Goals

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

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Introduction to intelligent systems	Textbook 1: Chapter 1	LO1
2	Introduction to artificial intelligence	Textbook 1: Chapter 1	LO1
3	Perceptron learning algorithms	Textbook 1: Chapter 2	LO2
4	Learning by back propagation of error	Textbook 1: Chapter 2	LO2
5	Artificial neural network (ANN) design and training	Textbook 1: Chapter 6	LO2
6	Use of neural networks for modeling and control	Textbook 1: Chapter 6	LO2
7	Introduction to fuzzy logic, fuzzy set theory	Textbook 1: Chapter 4	LO3
8	Midterm exam		-
9	Fuzzy composition and inference	Textbook 1: Chapter 4	LO3
10	Fuzzy control	Textbook 1: Chapter 4	LO3
11	Adaptive neuro-fuzzy inference system	Textbook 1: Chapter 8	LO3
12	Different combinations of neural networks and fuzzy systems	Textbook 1: Chapter 8	LO4
13	Particle swarm optimization and Genetic algorithm	Textbook 1: Chapter 7	LO5
14	Project presentations		-
15	Semester review		-
16	Final exam		-

Course Notes/Textbooks	Artificial Intelligence: A Guide to Intelligent Systems - Michael Negnevitsky - Second Edition 2005 Addison-Wesley. ISBN 0 321 20466 2
Suggested Readings/Materials	Ernest Davis - Douglas D. Edwards - David Forsyth - Nicholas J. Hay - Jitendra M. Malik - Vibhu Mittal - Mehran Sahami - Sebastian Thrun. Third Edition 2010 Editors: Stuart J. Russell and Peter Norvig. PRENTICE HALL.

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	1	10		X	X
Presentation / Jury	1	5				X	X
Project	1	15	X	X	X	X	X
Midterm	1	30	X	X	X
Final Exam	1	40	X	X	X	X	X
Total	5	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	16	3	48
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	1	12	12
Presentation / Jury	1	12	12
Project	1	20	20
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	15	15
Final Exam	1	25	25
		Total	180