| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | İŞD527 | Machine Learning | Elective | 1 | 1 | 6 |
|
| Level of Course Unit |
| Third Cycle |
| Objectives of the Course |
| 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 |
| Name of Lecturer(s) |
| Doç. Dr. Hakan PABUÇCU |
| Learning Outcomes |
| 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 |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| Intelligence and artificial intelligence, support vector machines, neural networks, decision trees, bayesian classifiers |
| Weekly Detailed Course Contents |
|
| 1 | Intelligent and artificial intelligence | | | | 2 | Decision trees | | | | 3 | Naive Bayes classifiers | | | | 4 | Learning with loss functions | | | | 5 | Neural networks | | | | 6 | Neural networks | | | | 7 | Neural networks | | | | 8 | Mid-term exam | | | | 9 | Support vector machine | | | | 10 | Support vector machine | | | | 11 | Feature selection technics | | | | 12 | Feature selection technics | | | | 13 | Selected neural networks algorithms | | | | 14 | Final Exam | | |
|
| Recommended or Required Reading |
| 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) |
| 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 | 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 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 2 | 3 | 3 | 3 | 3 | 4 | | LO2 | 3 | 3 | 5 | 2 | 4 | 5 | | LO3 | 3 | 5 | 4 | 3 | 5 | 1 | | LO4 | 5 | 4 | 4 | 4 | 5 | 2 | | LO5 | 3 | 5 | 4 | 5 | 4 | 4 |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
 |
| |