| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Artıfıcıal Intellıgence | ENM 417 | 7 | 3 + 0 | 3 | 5 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | Lisans |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Doç.Dr. SAFİYE SENCER |
| Dersi Verenler | Doç.Dr. SAFİYE SENCER, |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Öğretim |
| Dersin Amacı | Describing general structure of artificial intelligence and artificial intelligence’s algorithms, to teach artificial intelligence’s applications |
| Dersin İçeriği | Basic concepts (search, problem solving, knowledge representation methods, planning, natural language processing), artificial neural networks, expert systems, genetic algorithms, fuzzy logic. |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Understanding general structure of artificial intelligence | Lecture, Question-Answer, Discussion, Case Study, | Testing, Performance Task, |
| 2 | Understanding artificial neural networks | Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, | Testing, Homework, Performance Task, |
| 3 | Understanding expert systems | Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, | Testing, Homework, Performance Task, |
| 4 | Understanding genetic algorithms | Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, | Testing, Homework, Performance Task, |
| 5 | Understanding fuzzy logic | Lecture, Question-Answer, Discussion, Drilland Practice, Motivations to Show, Case Study, Problem Solving, | Testing, Homework, Performance Task, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Entrance to artificial intelligence | |
| 2 | Problem solving, natural language processing | |
| 3 | Knowledge representation methods | |
| 4 | Planning, search, vision, robotic, agent | |
| 5 | Entrance to artificial neural networks | |
| 6 | Artificial neural networks (Backpropagation) | |
| 7 | Artificial neural networks (LVQ network) | |
| 8 | Entrance to expert systems | |
| 9 | Expert systems | |
| 10 | Sample of expert system | |
| 11 | Entrance to genetic algorithms | |
| 12 | Sample of genetic algorithms | |
| 13 | Entrance to fuzzy logic | |
| 14 | Sample of fuzzy logic |
| Kaynaklar | |
|---|---|
| Ders Notu | |
| Ders Kaynakları | |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. | X | |||||
| 2 | Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. | X | |||||
| 3 | Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods. | X | |||||
| 4 | Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. | X | |||||
| 5 | Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. | X | |||||
| 6 | Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually | X | |||||
| 7 | Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language | X | |||||
| 8 | Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology | X | |||||
| 9 | Engineering graduates with well-structured responsibilities in profession and ethics | X | |||||
| 10 | Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. | X | |||||
| 11 | Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions. | X | |||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 55 |
| 1. Ödev | 15 |
| 2. Ödev | 15 |
| 3. Ödev | 15 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 60 |
| 1. Final | 40 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 2 | 32 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 12 | 12 |
| Assignment | 1 | 10 | 10 |
| Performance Task (Laboratory) | 1 | 14 | 14 |
| Toplam İş Yükü | 116 | ||
| Toplam İş Yükü / 25 (Saat) | 4,64 | ||
| Dersin AKTS Kredisi | 5 | ||