Faculty of Engineering and Natural Sciences · Industrial Engineering (English 30%) · Undergraduate
Course Objective
Artificial intelligence and its sub-concepts, teaching the basic principles of artificial intelligence techniques used in engineering applications, detailed analysis of how they are used in applications and teaching artificial intelligence optimization techniques.
Course Content
Definition of artificial intelligence, basic concepts and techniques, Expert Systems and engineering applications, Fuzzy logic and engineering applications, Artificial Neural Networks and application examples, Genetic algorithms and application examples, Artificial Immune System and application examples, Particle Swarm optimization and application examples, Artificial Immune Systems and application examples, Ant Colony Algorithm and application examples
Course Learning Outcomes
- Learn the basic principles and definitions of artificial intelligence and expert systems.
- Understand the use of artificial intelligence in industrial applications.
- Learn the basic principles of expert systems used in industrial applications.
- Gains knowledge about techniques in the field of artificial intelligence.
- Gains the ability to apply artificial intelligence and expert systems in industrial engineering applications.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 14 | 3 | 42 |
| Out of Class Study Period | 0 | 0 | 0 |
| Midterm | 1 | 2 | 2 |
| Quiz | 2 | 1 | 2 |
| Assignment | 5 | 3 | 15 |
| Practice | 3 | 2 | 6 |
| Final | 1 | 2 | 2 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Giriş: YZ(Yapay Zeka) ve Uzman Sistemler | No need. |
| 2 | Introduction: AI Smart Factors Examples of AI Languages | Chapter 1 from the resource book |
| 3 | Deciphering Problems with Search I | Chapter 2 |
| 4 | Deciphering Problems with Search II | Chapter 2 |
| 5 | Deciphering Problems with Search III | Chapter 3 |
| 6 | Expert Systems I | Chapter 4 |
| 7 | Midterm | Notes |
| 8 | Expert Systems II | Chapter 5 |
| 9 | Natural Language Processing | Chapter 6 |
| 10 | Machine Learning I | Chapter 7 |
| 11 | Machine Learning II | Chapter 7 |
| 12 | Bring Back Information | Chapter 8 |
| 13 | Project Presentations | Project |
| 14 | Project Presentations | Project |
| 15 | ||
| 16 |