Mechatronics Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Mechatronics Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Mechatronics Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05540004 | Introduction to Artificial Intelligence | 2 | Spring | 4 | 3+0+0 | 4 | 4 |
| Course Type | Compulsory |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Emre OFLAZ |
| Instructor Assistant(s) | Res. Asst. Gökberk AY |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Emre OFLAZ | A-228 | [email protected] | 7307 | Wednesday 15:00-17:00 |
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 | X |
| Specialization / Field Courses | |
| 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 |
|---|---|---|
| P4 | Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; Ability to use information technologies effectively | 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 issues and goals of artificial intelligence. | P.4.39 | 1 |
| O2 | Gaining the ability to use artificial intelligence methods to solve different problems | P.4.40 | 1,7 |
| ** 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 | Information about the structure of the brain, biological networks and nervous system |
| 4 | Learning and adaptation, neural network learning rules - 1 |
| 5 | Learning and adaptation, neural network learning rules - 2 |
| 7 | Dimensionality reduction |
| 8 | ANN application 1 |
| 9 | ANN application 2 |
| 10 | Application of Support Vector Machines |
| 11 | Model Tree Application |
| 12 | Ethic |
| 13 | Completion of project deficiencies |
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 | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 35 (%) |
| Quiz | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 35 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 12 | 3 | 36 |
| Midterms | 1 | 14 | 14 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 1 | 14 | 14 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 14 | 14 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 120 | ||
| Total Work Load / 30 | 4 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P4 |
|---|---|---|
| O1 | To understand the concept of artificial intelligence and rationality, to define the basic issues and goals of artificial intelligence. | 5 |
| O2 | Gaining the ability to use artificial intelligence methods to solve different problems | 5 |