Print

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.

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)14342
Hours for off-the-classroom study (Pre-study, practice)14342
Mid-terms12121
Final examination14040
Report / Project15050
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
 Select Year   


This course is not available in selected semester.


Print

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes