| Dersin Kodu | Dersin Adı | Dersin Türü | Yıl | Yarıyıl | AKTS | | İDR527 | Machine Learning | Seçmeli | 1 | 1 | 6 |
|
| Dersin Seviyesi |
| Doktora |
| Dersin Amacı |
| Gains general knowledge about machine learning techniques.
Learns the working principle of selected machine learning techniques.
It can make inferences about the usage areas of machine learning in the field of social sciences.
Learns the mathematical structure of the selected techniques.
Interpret the findings obtained as a result of the analyzes |
| Dersi Veren Öğretim Görevlisi/Görevlileri |
| Doç. Dr. Hakan PABUÇCU |
| Öğrenme Çıktıları |
| 1 | Gains general knowledge about machine learning techniques. | | 2 | Learns the working principle of selected machine learning techniques. | | 3 | Makes inferences about ML technics usage areas in social sciences. | | 4 | Learns the mathematical structure of the selected techniques. | | 5 | Interpret the findings obtained as a result of the analyzes |
|
| Öğrenim Türü |
| Normal Education |
| Dersin Ön Koşulu Olan Dersler |
| None |
| Ders İçin Önerilen Diğer Hususlar |
| None |
| Dersin İçeriği |
| Intelligence and artificial intelligence, support vector machines, neural networks, decision trees, bayesian classifiers |
| Haftalık Ayrıntılı Ders İçeriği |
|
| 1 | Intelligent and artificial intelligence | | | | 2 | Decision trees | | | | 3 | Naive Bayes classifiers | | | | 4 | Learning with loss functions | | | | 5 | Neural networks | | | | 6 | Neural networks | | | | 7 | Mid-term exam | | | | 8 | Neural networks | | | | 9 | Support vector machine | | | | 10 | Support vector machine | | | | 11 | Feature selection technics | | | | 12 | Feature selection technics | | | | 13 | Selected neural networks algorithms | | | | 14 | General overview | | |
|
| Ders Kitabı / Malzemesi / Önerilen Kaynaklar |
| Öztemel, E. (2003). Yapay sinir ağlari. PapatyaYayincilik.
Elmas, Ç. (2018). Yapay zeka uygulamaları., Seçkin yayıncılık.
The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. H.
Friedman (publisher: Springer)
http://www.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf
Pattern Recognition and Machine Learning by Christopher M. Bishop
(publisher: Springer)
Machine Learning by Tom M. Mitchell (publisher: McGraw-Hill) |
| Planlanan Öğrenme Aktiviteleri ve Metodları |
|
| Değerlendirme | |
| Midterm Examination | 1 | 100 | | TOPLAM | 100 | |
| Final Examination | 1 | 100 | | TOPLAM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | TOPLAM | 100 |
| | Dersin Sunulduğu Dil | | Turkish | | Staj Durumu | | None |
|
| İş Yükü Hesaplaması |
|
| Midterm Examination | 1 | 1 | 1 |
| Final Examination | 1 | 2 | 2 |
| Attending Lectures | 14 | 3 | 42 |
| Practice | 1 | 20 | 20 |
| Field Work | 1 | 20 | 20 |
| Self Study | 1 | 30 | 30 |
| Individual Study for Mid term Examination | 1 | 30 | 30 |
| Individual Study for Final Examination | 1 | 30 | 30 |
|
| Program ve Öğrenme Çıktıları İlişkisi |
| ÖÇ1 | 2 | 3 | 3 | 3 | 3 | 4 | | ÖÇ2 | 3 | 3 | 5 | 2 | 4 | 5 | | ÖÇ3 | 3 | 5 | 4 | 3 | 5 | 1 | | ÖÇ4 | 5 | 4 | 4 | 4 | 5 | 2 | | ÖÇ5 | 3 | 5 | 4 | 5 | 4 | 4 |
|
| * Katkı Düzeyi : 1 Çok düşük 2 Düşük 3 Orta 4 Yüksek 5 Çok yüksek |
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