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Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Mechatronics Engineering Master of Science
Course Details
Course Code Course Name Year Period Semester T+A+L Credit ECTS
81811106 Artificial Intelligence Systems 1 Autumn 1 3+0+0 7 7
Course Type Elective
Course Cycle -
Course Language Turkish
Methods and Techniques -
Mode of Delivery Face to Face
Prerequisites -
Coordinator -
Instructor(s) Asst. Prof. Amir YAVARIABDI
Instructor Assistant(s) -
Course Content
Yapay zekaya giriş, Doğal ve Yapay Zeka, Arama yöntemleri, Planlama, Sezgisel Problem Çözme, Bilgi gösterilimi, Yüklem Mantığı, Denetimli Öğrenme Algoritmaları, Yapay Zeka Programlama Dilleri, Uzman Sistemler, Yapay Zeka Uygulamaları.
Objectives of the Course
To study the main concepts and techniques used in Artificial intelligence and learn to apply these techniques in different problems.
Contribution of the Course to Field Teaching
Basic Vocational Courses
Specialization / Field Courses X
Support Courses
Transferable Skills Courses
Humanities, Communication and Management Skills Courses
Relationships between Course Learning Outcomes and Program Outcomes
Relationship Levels
Lowest Low Medium High Highest
1 2 3 4 5
# Program Learning Outcomes Level
P2 Know the applications of mathematics in engineering 4
P5 Ability to do patent searching and literature research. 4
P7 Ability to propose innovative solution according to basic science and technological developments. 5
Course Learning Outcomes
Upon the successful completion of this course, students will be able to:
No Learning Outcomes Outcome Relationship Measurement Method **
O1 To understand the concept of artificial intelligence and rationality, to define the basic subjects and purposes of artificial intelligence. P.2.3 1
O2 To be able to define search problems and to apply ignorant and intuitive search techniques to search problems. P.7.6 7
O3 Developing AI algorithms for difficult problems P.7.7 7
O4 Having intermediate level knowledge in MATLAB and Python programming languages P.5.2 7
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7
Weekly Detailed Course Contents
Week Topics
1 Introduction to Artificial Intelligence
2 Regression
3 Regression - Maximum Likelihood Estimation
4 Decision Tree
5 Random Forest
6 Implementing Decision Tree and Random Forest
7 Dimensionality reduction
8 KNN and implementation
9 Naive Bayes classifier
10 AutoEncoder
11 Generative Adversarial Networks
12 neural network
13 Convolutional neural network
Textbook or Material
Resources Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed., Pearson.
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed., Pearson.
Evaluation Method and Passing Criteria
In-Term Studies Quantity Percentage
Attendance - -
Laboratory - -
Practice - -
Homework - -
Presentation - -
Projects 1 30 (%)
Seminar - -
Quiz - -
Midterms - -
Final Exam 1 70 (%)
Total 100 (%)
ECTS / Working Load Table
Quantity Duration Total Work Load
Course Week Number and Time 13 19 247
Out-of-Class Study Time (Pre-study, Library, Reinforcement) 0 0 0
Midterms 0 0 0
Quiz 0 0 0
Homework 0 0 0
Practice 0 0 0
Laboratory 0 0 0
Project 0 0 0
Workshop 0 0 0
Presentation/Seminar Preparation 1 3 3
Fieldwork 0 0 0
Final Exam 1 2 2
Other 0 0 0
Total Work Load: 252
Total Work Load / 30 8,40
Course ECTS Credits: 8
Course - Learning Outcomes Matrix
Relationship Levels
Lowest Low Medium High Highest
1 2 3 4 5
# Learning Outcomes P2 P5 P7
O1 To understand the concept of artificial intelligence and rationality, to define the basic subjects and purposes of artificial intelligence. 4 - -
O2 Having intermediate level knowledge in MATLAB and Python programming languages - 4 -
O3 To be able to define search problems and to apply ignorant and intuitive search techniques to search problems. - - 5
O4 Developing AI algorithms for difficult problems - - 5