TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Professor Yusuf Tansel İç Assistant Professor Mehmet Dikmen
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand scientifıc and mathematical principles and apply them to the practice of engineering. 2) Gain an ability to use Computer programs as tools for system design and analysis applications. 3) Have an ability to apply the advanced principles of measurement. data analysis, and design of experiments. 4) Have an ability to apply the system/process design levels to industrial engineering problems, including the consideration of different technical alternatives while bearing in mind cost, environmental concems, safety, and other constraints. 5) Have an ability to analyze, measure, test, and evaluate an industrial engineering problem.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Theoretical foundations of artificial neural networks, tools of artificial neural networks, learning algorithms in artificial neural networks and architecture, modeling of artificial neural networks, verification analysis, forecasting with artificial neural networks, applications in Industrial Engineering.
COURSE CONTENTS	WEEK TOPICS 1st Week The basic structure of artificial neural Networks, advantages and disadvantages 2nd Week Similarity between human brain and neural networks 3rd Week Neural Netvvork Pattems: Biological neurons, Artificial nerve celi 4th Week Neural Network Patterns : Basic components 5th Week Leaming in Artificial Neural Networks: Basic learning rules 6th Week Leaming in Artificial Neural Networks: Basic learning rules 7th Week ANN Architectures 8th Week ANN Modelling 9th Week Midterm 10th Week ANN Verification Analysis 11th Week Industrial Engineering Applications with ANN 12th Week Industrial Engineering Applications with ANN 13th Week Forecasting with ANN 14th Week Forecasting Applications with ANN
RECOMENDED OR REQUIRED READING	Fausett, L., Fundementals ofNeural Networks, 1994. Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte Yapay Zeka Uygulamaları-1 Yapay Sinir Ağları, Ufuk Yayıncılık, 2003.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Presentation,Other,Lecture
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 25 Assignment 1 10 Quiz 1 15 Practice 1 15 Total(%) 65 Contribution of In-term Studies to Overall Grade(%) 65 Contribution of Final Examination to Overall Grade(%) 35 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz 1 4 4 Individual or group work 14 2 28 Preparation for Final exam 1 25 25 Course hours 14 4 56 Preparation for Midterm exam 1 25 25 Laboratory (including preparation) 1 2 2 Final exam 1 2 2 Homework 1 8 8 Total Workload 152 Total Workload / 30 5,06 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No