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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
ARTIFICIAL INTELLIGENCE STS614 - 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITDoctorate Of Science
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) Be informed of Knowledge representation and reasoning.
2) Know syntax, semantics, and proof theory (deductive inference) of propositional logic.
3) Apply first-order predicate logic.
4) Have knowledge of uncertainty and probabilistic reasoning.
5) Recognize expert systems.
MODE OF DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTnone
COURSE DEFINITIONKnowledge representation and reasoning. Syntax, semantics. Proof theory (deductive inference) of propositional logic. First-order predicate logic. Uncertainty. Probabilistic reasoning. Expert systems.
COURSE CONTENTS
WEEKTOPICS
1st Week Knowledge representation and reasoning.
2nd Week Knowledge representation and reasoning.
3rd Week Knowledge representation and reasoning.
4th Week Syntax, semantics.
5th Week Syntax, semantics.
6th Week Syntax, semantics.
7th Week Syntax, semantics.
8th Week Mid-term
9th Week Proof theory (deductive inference) of propositional logic. First-order predicate logic.
10th Week Proof theory (deductive inference) of propositional logic. First-order predicate logic.
11th Week Uncertainty.
12th Week Probabilistic reasoning.
13th Week Expert systems.
14th Week Expert systems.
RECOMENDED OR REQUIRED READING1. Russell S., Norvig P. Artificial Intelligence: A Modern Approach, Prentice Hall, 2003, Second Edition, ISBN: 9780137903955
PLANNED LEARNING ACTIVITIES AND TEACHING METHODSLecture,Questions/Answers,Experiment,Report Preparation,Practice,Problem Solving,Project
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term130
Assignment115
Project115
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 work148112
Preparation for Final exam16060
Course hours14342
Preparation for Midterm exam12525
Laboratory (including preparation)
Final exam122
Homework21020
Project13636
Total Workload299
Total Workload / 309,96
ECTS Credits of the Course10
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
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K11