Introduction To Artificial Intelligence
Aircraft Airframe and Powerplant Maintenance
Faculty of Aeronautics and Astronautics
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Introduction To Artificial Intelligence | HUF415 | Elective | Bachelor's degree | 4 | Fall | 6 |
Name of Lecturer(s)
Assistant Prof. Dr. Ramazan DUVAR
Learning Outcomes of the Course Unit
1) Use artificial intelligence tools
2) Gain up-to-date knowledge on artificial intelligence
3) Use known artificial intelligence algorithms to solve given problems
4) To be able to improve the knowledge learned in the course and offer new solutions unique to the problems
5) Re-arrange and optimize machine learning models.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | ||
| Learning Outcomes | ||||||||||||||||||||
| 1 | Low | High | No relation | No relation | No relation | High | Middle | No relation | No relation | Middle | No relation | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 2 | Low | High | No relation | No relation | No relation | High | Middle | No relation | No relation | Middle | No relation | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 3 | Low | High | No relation | No relation | No relation | High | Middle | No relation | No relation | Middle | No relation | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 4 | Low | High | No relation | No relation | No relation | High | Middle | No relation | No relation | Middle | No relation | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 5 | Low | High | No relation | No relation | No relation | High | Middle | No relation | No relation | Middle | No relation | Middle | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
e-course
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
This course covers below topics; 1. Introduction to Artificial Intelligence, Basic Terms 2. Search alghorithms 3. Heuristic Algorithms 4. Supervised / Unsupervised Learning 5. Classification and Linear regression 6. The Nearest Neighbor Method 7. Clustering Methods 8. Support Vector Machines 9. Decision trees 10. Artificial Neural Networks 11. Deep Learning
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
50% |
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Contribution of Final Examination to Course Grade |
50% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required