1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. COMPUTER ENGINEERING DEPARTMENT
  4. 1281 Computer Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 512ARTIFICIAL INTELLIGENCE AND ENGINEERING APPLICATIONS3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective Nowadays, computer designs modelling human brain has taken attention. In this course, learning techniques are examinated and given some capable about smart system design.
Course Content Knowledge Representation: Knowledge Level Methods (Rule Based, Logic Based, and Frame Representation); Symbol Level Methods (Representations in Semantic Nets, Classifiers and Genetic Algorithms); Device Level Methods (Representations in Perceptrons and Neural Networks). Knowledge Based Systems: Expert Systems - History, General Structure and Development. General Knowledge Systems: CYC Methodology and Development. Intelligent Agents: Agent Environment, Agent Components and Agent Architecture.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Lists Artificial Intelligence(AI) concepts
2Differentiates between traditional programming and AI programming
3Explains some machine learning methods
4Lists variations of expert system
5Represents Natural Language Processing

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 00152432       
LO 00252432       
LO 00352432       
LO 00452432       
LO 00552432       
Sub Total2510201510       
Contribution524320000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)15345
Hours for off-the-classroom study (Pre-study, practice)15460
Mid-terms11515
Final examination13030
Report / Project31545
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Fall1SEZAİ TOKAT


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
CENG 512 ARTIFICIAL INTELLIGENCE AND ENGINEERING APPLICATIONS 3 + 0 1 Turkish 2023-2024 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Prof. Dr. SEZAİ TOKAT stokat@pau.edu.tr MUH A0257 %70
Goals Nowadays, computer designs modelling human brain has taken attention. In this course, learning techniques are examinated and given some capable about smart system design.
Content Knowledge Representation: Knowledge Level Methods (Rule Based, Logic Based, and Frame Representation); Symbol Level Methods (Representations in Semantic Nets, Classifiers and Genetic Algorithms); Device Level Methods (Representations in Perceptrons and Neural Networks). Knowledge Based Systems: Expert Systems - History, General Structure and Development. General Knowledge Systems: CYC Methodology and Development. Intelligent Agents: Agent Environment, Agent Components and Agent Architecture.
Topics
WeeksTopics
1 Introduction to artificial intelligence
2 Intelligent agent
3 Arama Uzayı
4 Heuristic search
5 Genetic algorithms
6 Knowledge representation
7 Logical programming
8 Bayesian Learning
9 Game Theory
10 Projects and Applications
11 Projects and Applications
12 Projects and Applications
13 Projects and Applications
14 Projects and Applications
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes