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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS END426 - 3 + 0 5

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITBachelor's Degree
YEAR OF STUDY-
SEMESTER-
NUMBER OF ECTS CREDITS ALLOCATED5
NAME OF LECTURER(S)Assistant Professor Mehmet Dikmen
LEARNING OUTCOMES OF THE COURSE UNIT At the end of this course, the students;
1) Have the ability to understand scientific and mathematical principles and apply them to the practice of engineering.
2) Have the ability to use computer programs as tools for analysis applications
3) Have the ability to apply the advanced principles of measurement, data analysis, and design of experiments
4) Have the 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) Have the ability to analyze, measure, test, and evaluate an industrial engineering problem
MODE OF DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTNone
COURSE DEFINITIONKnowledge representation. Rule-base systems. Knowledge acquisition, decision trees, ID3 algorithm. The inference engine: forward chaining, backward chaining, and backward chaining algorithms. Inexact reasoning, uncertainty models in expert systems. Validity of knowledge base. Fuzzy expert systems. Frame-based expert systems. Hybrid systems.
COURSE CONTENTS
WEEKTOPICS
1st Week Expert systems: understanding. Basic concept and structure of ES.
2nd Week Knowledge representaton. Representation via rule-based systems.
3rd Week Knowledge representation. Implementation rules.
4th Week Knowledge acquisition. Examples of knowledge acquisition.
5th Week The inference engine. The forward chaining algorithm.
6th Week The inference engine. The backward chaining algorithm.
7th Week Inexact reasoning. Uncertainty management in expert systems.
8th Week Validity of knowledge base. Examples of checking.
9th Week Midterm
10th Week Hybrid expert systems. Practical considerations.
11th Week Fuzzy expert systems. Fuzzy rules and inference.
12th Week Frame-based expert systems. Designing issues.
13th Week Knowledge engineering. Problem assessment,Design and testing.
14th Week Knowledge-based system projects.
RECOMENDED OR REQUIRED READINGJames P. Ignizio. Introduction to Expert Systems. The Development and Implementation of Rule-Based Expert Systems, McGraw-Hill, Inc., 1991;
Michael Negnevitsky. A Guide to Intelligent Systems, Addison-Wesley, 2001
PLANNED LEARNING ACTIVITIES AND TEACHING METHODSQuestions/Answers,Lecture,Practice,Presentation,Problem Solving
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Total(%)0
Contribution of In-term Studies to Overall Grade(%)0
Contribution of Final Examination to Overall Grade(%)100
Total(%)100
ECTS WORKLOAD
Activities Number Hours Workload
Midterm exam
Preparation for Quiz
Individual or group work14570
Preparation for Final exam13030
Course hours14342
Preparation for Midterm exam
Laboratory (including preparation)
Final exam122
Homework
Total Workload144
Total Workload / 304,8
ECTS Credits of the Course5
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
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K4    X       X
K5      X    
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K11        X