| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 7 | COMP465 | ARTIFICIAL INTELIGENCE | 0+3+0 | 3 | 5 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Gain an understanding of artificial intelligence methodologies
Learn the techniques used for developing artificial intelligence models
Gain practice by coding programming assignments
Apply the concepts to a real problem by completing a course project
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Course Content
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This course provides an introduction to Artificial Intelligence. In this course, we will learn the concepts that underlie intelligent systems. Topics we will cover include problem solving with search, constraint satisfaction, knowledge representation and reasoning using some probabilistic learnings and first order logics, reasoning under uncertainty, introduction to machine learning, and introduction to reinforcement learning.
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Course Methods and Techniques
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|
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Prerequisites and co-requisities
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None
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Course Coordinator
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None
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Name of Lecturers
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Asist Prof.Dr. VEHBİ ÇAĞRI GÜNGÖR zafer.aydin@agu.edu.tr
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
ECTS Allocated Based on Student Workload
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Activities
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Total Work Load
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Yazılı Sınav
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1
|
10
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10
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F2F Dersi
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1
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3
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3
|
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Ev Ödevi
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1
|
5
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5
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Sunum için Hazırlık
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1
|
5
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5
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Proje
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1
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20
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20
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Kısa Sınav
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1
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1
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1
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Okuma
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1
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1
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1
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Final Sınavı
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1
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20
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20
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Total Work Load
| |
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Number of ECTS Credits 2
65
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
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1
| Explain the mathematical and algorithmic principles of artificial intelligence models |
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2
| Solve a machine learning problem using artificial intelligence methods |
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3
| Implement a reinforcement learning model using a software |
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4
| Apply a deep learning method to a real problem |
Weekly Detailed Course Contents
Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=74870&lang=en