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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
EXPERT SYSTEMS BİL576 - 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITMaster's Degree With Thesis
YEAR OF STUDY-
SEMESTER-
NUMBER OF ECTS CREDITS ALLOCATED10
NAME OF LECTURER(S)-
LEARNING OUTCOMES OF THE COURSE UNIT At the end of this course, the students;
1) Learn knowledge representation and rule-based systems.
2) Understand decision trees and ID3 algorithm.
3) Learn forward and backward chaining algortihms.
4) Gain skills about methods of solving problems related to artificial intelligence and expert systems.
5) Gain experience to apply expert systems.
6) Design an expert system.
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. Theory of expert systems. Design a expert system. Validity of knowledge base. Fuzzy expert systems. Frame-based expert systems. Hybrid Expert Systems.
COURSE CONTENTS
WEEKTOPICS
1st Week Knowledge representation.
2nd Week Rule-base systems.
3rd Week Knowledge acquisition
4th Week Decision trees, ID3 algorithm.
5th Week The inference engine: forward chaining, backward chaining, and backward chaining algorithms.
6th Week Inexact reasoning
7th Week Uncertainty models in expert systems.
8th Week Mid-term
9th Week Theory of expert systems
10th Week Design a expert system
11th Week Validity of knowledge base.
12th Week Fuzzy expert systems.
13th Week Frame-based expert systems.
14th Week Hybrid Expert Systems.
RECOMENDED OR REQUIRED READING1. Jackson P., Introduction To Expert Systems, 3/E, Addision Wesley, 1998
2. Hayes- Roth F., Waterman D.A., Lenat D.B, Building Expert Systems, ISBN: 0201106868, Addison-Wesley, 1983
3. Turban E., Sharda R., Delen D., Decision Support and Business Intellegence Systems, 9/E, ISBN:9780132453233, Pearson, 2011
PLANNED LEARNING ACTIVITIES AND TEACHING METHODSLecture,Questions/Answers,Presentation,Problem Solving
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term130
Quiz130
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 exam122
Preparation for Quiz
Individual or group work1411154
Preparation for Final exam16969
Course hours14342
Preparation for Midterm exam14444
Laboratory (including preparation)
Final exam122
Homework
Total Workload313
Total Workload / 3010,43
ECTS Credits of the Course10
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
LO1LO2LO3LO4LO5LO6
K1  X   X   X   X   X   X
K2            X
K3            X
K4        X   X   X
K5            X
K6            X
K7           
K8           
K9           
K10            X
K11            X
K12