TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Assistant Professor Mehmet Dikmen
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand scientific 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) Gain an ability to apply the advanced principles of measurement, data analysis, and design of experiments 4) Gain 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 concerns, safety, and other constraints. 5) Gain 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
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to neural Networks 2nd Week Similarity between human brain and neural networks 3rd Week Neural Network Patterns 4th Week Neural Network Patterns 5th Week Learning algorithms 6th Week Learning algorithms 7th Week ANN Architectures 8th Week Midterm 9th Week ANN Modelling 10th Week ANN Verification Analysis 11th Week Engineering Applications with ANN 12th Week Engineering Applications with ANN 13th Week Forecasting with ANN 14th Week Optimization with ANN
RECOMENDED OR REQUIRED READING	1. Fausett, L. , Fundementals of Neural Networks, 1994. 2. Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte Yapay Zeka Uygulamaları-1 Yapay Sinir Ağları, Ufuk Yayıncılık, 2003.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Presentation,Questions/Answers,Project
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Project 1 30 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 14 196 Preparation for Final exam 1 25 25 Course hours 14 3 42 Preparation for Midterm exam 1 25 25 Laboratory (including preparation) Final exam 1 2 2 Homework 1 14 14 Total Workload 306 Total Workload / 30 10,2 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No