Expert Systems (IE416) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Expert Systems IE416 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Observation Case Study, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives This course will provide students with the skills needed to identify appropriate areas for the application of expert system technologies and to familiarize them with the methodologies and tools used in industrial engineering. Students should be able to recognize organizational and societal impacts of expert system technologies in service and/or production environments. Students should be aware of cost considerations and implementation strategies.
Course Learning Outcomes The students who succeeded in this course;
  • Students will be able to make use of ES steps such as knowledge acquisition and knowledge validation representation.
  • Students will be able to design a knowledge structured integrated system for a variety of production and operations management modules.
  • Students will use various knowledge representation methods and export system structures for industrial engineering purposes.
  • Students will be able to follow the developments in AI and ES supporting the industrial engineering area.
Course Content Technology of expert systems and applications; development of a simple expert system; artificial intelligence concepts, heuristics, problem solving, intelligent attributes; use of expert systems in industry; intelligent decision support systems; case studies about engineering environments.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Fundamentals of expert systems
2 Knowledge acquisition and knowledge validation representation
3 Knowledge acquisition and knowledge validation representation
4 The tools for building efficient expert systems for industrial engineering applications
5 The tools for building efficient expert systems for industrial engineering applications
6 User interface and design issues and integration with decision support system
7 User interface and design issues and integration with decision support system
8 Midterm I
9 Basic concepts and procedures on how to select, initiate, implement, and manage the the expert system and how to cope with uncertainty
10 Basic concepts and procedures on how to select, initiate, implement, and manage the the expert system and how to cope with uncertainty
11 Evaluation of expert systems approaches
12 Evaluation of expert systems approaches Midterm II
13 Use of expert systems in industry, intelligent decision support systems, case studies in industrial engineering applications
14 Use of expert systems in industry, intelligent decision support systems, case studies in industrial engineering applications
15 The future of expert systems
16 Final Examination Period

Sources

Course Book 1. Jackson, P., Introduction to Expert Systems, Addison-Wesley, 1998
Other Sources 2. Durkin, J., Expert Systems Design and Development, Macmillan Publishing Company, 1994. Sillince, J., Business Expert Systems , Prentice Hall Professional Technical Reference, 1997 Liebowitz, J. and Letsky, C., Developing Your First Expert System - An Inte

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 60
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
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 An ability to apply knowledge in mathematics and basic sciences and computational skills to solve manufacturing engineering problems
2 An ability to define and analyze issues related with manufacturing technologies
3 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment
4 An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints
5 An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications
6 An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly
7 An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually
8 An ability to attain efficient communication skills in Turkish and English both verbally and orally
9 An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology
10 An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering
11 An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development
12 An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process

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 10 1 10
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 20 40
Prepration of Final Exams/Final Jury 1 27 27
Total Workload 125