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| YZLM5200 | machine learning | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Asuman GÜNAY YILMAZ | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To provide information about the latest developments in the field of Machine Learning, to gain experience by applying algorithms to real data sets, and to gain the ability to read and understand articles in the current literature. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Apply the essential principles of machine learning concept to real-life data. | 1,3,4 | 1,3, | | PO - 2 : | Identify the differences between machine learning methods and gain the skills of selecting an appropriate method for a given data. | 1,3,4 | 1,3, | | PO - 3 : | Analyze the performance and the results of a machine learning method in terms of error complexity. | 1,3,4 | 1,3, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Supervised learning, Unsupervised Learning, SA, k-NN, Bayes, Computational Learning Theory, |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction | | | Week 2 | Supervised learning | | | Week 3 | Bayesian learning | | | Week 4 | Model selection | | | Week 5 | neural network | | | Week 6 | nearest neighbor | | | Week 7 | Naïve Bayes | | | Week 8 | Support vector machines | | | Week 9 | Midterm exam | | | Week 10 | Decision trees | | | Week 11 | Experimental design and evaluation | | | Week 12 | Computational learning theory | | | Week 13 | Ensemble methods | | | Week 14 | Unsupervised learning | | | Week 15 | Unsupervised learning | | | Week 16 | Final exam | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 20 | | Homework/Assignment/Term-paper | 14 | | 1 | 30 | | End-of-term exam | 15 | | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 6 | 14 | 84 | | Arasınav için hazırlık | 3 | 2 | 6 | | Arasınav | 2 | 1 | 2 | | Uygulama | 3 | 10 | 30 | | Ödev | 2 | 10 | 20 | | Dönem sonu sınavı için hazırlık | 3 | 2 | 6 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 192 |
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