| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | İŞ173 | Artificial Intelligence Applications | Elective | 1 | 1 | 6 |
|
| Level of Course Unit |
| Second Cycle |
| Objectives of the Course |
| The aim of this course is to provide students who do not have the scientific knowledge about artificial intelligence techniques to have information about selected technics that they can use in their graduate studies, especially in the field of finance and economics. First of all, students who have theoretical knowledge about the selected methods will be able to apply the methods to the data sets and interpret the results they have obtained. |
| Name of Lecturer(s) |
| Doç. Dr. Hakan PABUÇCU |
| Learning Outcomes |
| 1 | Gains general knowledge about artificial intelligence techniques. | | 2 | Learns the working principle of selected artificial intelligence techniques. | | 3 | Makes inference about the usage areas of artificial intelligence in the field of social sciences. | | 4 | Learns the mathematical structure of the selected techniques. | | 5 | Interpret the findings obtained as a result of the analyzes |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| Intelligence and artificial intelligence, artificial neural networks, construction of artificial neural networks, artificial neural network architectures, learning algorithms, recent developments and new artificial neural network models |
| Weekly Detailed Course Contents |
|
| 1 | Intelligence and artificial intelligence | | | | 2 | Artificial neural networks introduction | | | | 3 | Artificial neural networks | | | | 4 | Artificial neural networks | | | | 5 | Neural network architectures | | | | 6 | Neural network architectures | | | | 7 | Mid-term exam | | | | 8 | Presentations | | | | 9 | Learning algorithms, | | | | 10 | Learning algorithms, | | | | 11 | Learning algorithms, | | | | 12 | Son gelişmeler ve yeni yapay sinir ağı modelleri | | | | 13 | Presentations | | | | 14 | final exam | | |
|
| Recommended or Required Reading |
| Öztemel, E. (2003). Yapay sinir ağlari. PapatyaYayincilik.
Elmas, Ç. (2018). Yapay zeka uygulamaları., Seçkin yayıncılık.
Simon, H. (1999). Neural networks: a comprehensive foundation. Prentice hall. |
| Planned Learning Activities and Teaching Methods |
|
| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
|
| Workload Calculation |
|
| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 2 | 2 |
| Attending Lectures | 14 | 3 | 42 |
| Practice | 1 | 30 | 30 |
| Self Study | 1 | 30 | 30 |
| Individual Study for Mid term Examination | 1 | 30 | 30 |
| Individual Study for Final Examination | 1 | 30 | 30 |
| Performance | 1 | 20 | 20 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 3 | 1 | 2 | 5 | 4 | 5 | | LO2 | 3 | 1 | 2 | 5 | 4 | 4 | | LO3 | 2 | 1 | 1 | 4 | 5 | 4 | | LO4 | 3 | 1 | 1 | 4 | 4 | 5 | | LO5 | 2 | 1 | 1 | 5 | 5 | 5 |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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