| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | MY102Z | Artificial Intelligence in Engineering | Elective | 1 | 1 | 6 |
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| Level of Course Unit |
| Second Cycle |
| Objectives of the Course |
| Teaching artificial intelligence applications by giving general structure and algorithms of Artificial Intelligence. |
| Name of Lecturer(s) |
| Dr. Öğr. Üyesi Çağatay TEKE |
| Learning Outcomes |
| 1 | grasp the general structure of Artificial Intelligence | | 2 | grasp the Artificial Neural Networks | | 3 | grasp the Expert Systems | | 4 | grasp the Genetic Algorithms | | 5 | grasp the Fuzzy Propositional Logic |
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| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
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| Recommended Optional Programme Components |
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| Course Contents |
| Basic concepts (search, problem solving, knowledge representation methods, planning, natural language processing), artificial neural networks, expert systems, genetic algorithms, fuzzy propositional logic. |
| Weekly Detailed Course Contents |
|
| 1 | Introduction to artificial intelligence | | | | 2 | Problem Solving, Natural Language Processing | | | | 3 | Planning, Search, Vision, Robotics, | | | | 4 | Knowledge Representation Methods | | | | 5 | General introduction to expert Systems | | | | 6 | Expert Systems | | | | 7 | Expert systems example | | | | 8 | Midterm Exam | | | | 9 | General introduction to fuzzy Propositional Logic | | | | 10 | Fuzzy propositional logic example | | | | 11 | General introduction to Artificial Neural Networks | | | | 12 | Artificial Neural Networks (Multi-layer Sensors-Backpropagation) | | | | 13 | Example of Artificial Neural Networks | | | | 14 | Artificial Neural Networks (LVQ Network) | | | | 15 | Genetic Algorithms | | |
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| Recommended or Required Reading |
| Russell S., Norvig P., 2002, "Artificial Intelligence: A modern approach", Prentice Hall series in Artificial Intelligence, 2nd Edition
Luger G.F., 2004, "Artificial Intelligence: Structures and Strategies for Complex Problem Solving", Addison-Wesley, 5th Edition
Uzman sistemler: Bir Yapay Zeka Uygulaması, Novruz Allahverdi, 2002
Kusiak, A., Intelligent Manufacturing Systems, Prentice Hall International Editions, New Jersey, 1990.
Patterson D.W., 1990, "Introduction to artificial intelligence and expert systems", Prentice Hall |
| Planned Learning Activities and Teaching Methods |
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| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 30 | | End Of Term (or Year) Learning Activities | 70 | | SUM | 100 |
| | Language of Instruction | | | Work Placement(s) | |
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| Workload Calculation |
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| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 2 | 2 |
| Self Study | 14 | 3 | 42 |
| Individual Study for Mid term Examination | 6 | 6 | 36 |
| Individual Study for Final Examination | 10 | 10 | 100 |
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| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 4 | 2 | 3 | 4 | 3 | | 3 | 4 | | | | | | LO2 | 4 | 5 | 4 | 3 | 4 | | 5 | | | | | | | LO3 | 3 | 5 | 4 | 4 | 4 | | 5 | | | | | | | LO4 | 4 | 5 | 4 | 4 | 4 | 4 | 5 | | | | | | | LO5 | 5 | 5 | 4 | 3 | 4 | 4 | 5 | | | | | |
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| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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