ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Computer Technologies (Evening Education) | |||||
| Associate | TR-NQF-HE: Level 5 | QF-EHEA: Short Cycle | EQF-LLL: Level 5 | ||
Course Introduction and Application Information
| Course Code: | MYO032 | ||||
| Course Name: | Artificial Intelligence Fundamentals | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Öğr. Gör. RAHİME BÜŞRA HACIMUSTAFAOĞLU | ||||
| Course Lecturer(s): |
Öğr. Gör. BURAK AĞGÜL |
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| Course Assistants: |
Course Objective and Content
| Course Objectives: | Understanding the basic concepts and applications of artificial intelligence, determining learning algorithms with the appropriate optimization algorithm to be used in the analysis of problems, and interpreting the results obtained with the help of sample problems. |
| Course Content: | Introduction to artificial intelligence and basic concepts, problem analysis and solution, learning, different artificial intelligence algorithms, optimization algorithms, image analysis, genetic algorithm. |
Learning Outcomes
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The students who have succeeded in this course;
1) Understanding the basics of artificial neural network structures. 2) Understanding statistical learning. 3) Understanding the difference between machine learning algorithms. 4) Ability to write programs using artificial intelligence algorithms 5) Understanding the optimization algorithms required for machine learning |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Artificial Intelligence | Course notes |
| 2) | Artificial neural networks | |
| 3) | Statistical Learning | |
| 4) | Machine Learning | |
| 5) | Deep Learning | |
| 6) | Supervised Learning | |
| 7) | Unsupervised Learning | |
| 8) | Midterm Exam | |
| 9) | Reinforcement Learning | |
| 10) | Natural Language Processing | |
| 11) | Support Vector Machines | |
| 12) | Computer Vision | |
| 13) | Genetic Algorithm | |
| 14) | Robotik | |
| 15) | Final Exam |
Sources
| Course Notes / Textbooks: | - |
| References: | Ders Notları |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 2 | 20 | 40 | ||||
| Midterms | 1 | 15 | 15 | ||||
| Final | 1 | 20 | 20 | ||||
| Total Workload | 75 | ||||||