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| USEC0027 | Teakwando | 2+0+0 | ECTS:4 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of INTERNATIONAL RELATIONS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Öğr. Gör. Dr Burakhan AYDEMİR | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | |
To get the ability of the use of machine learning techniques with Phyton in the analysis of health data.
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| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Ability to cluster health data with Python | | | | LO - 2 : | Ability to classify health data with Python | | | | LO - 3 : | To be able to realize KNN, linear regression methods on health data with Python | | | | LO - 4 : | To be able to implement Naive Bayes classifier on health data with Python | | | | LO - 5 : | To be able to implement Neural Networks and SVM methods on health data with Python | | | | 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), LO : Learning Outcome | | |
| Basic knowledge of Python programming language, KNN classification, Linear regression, Naive Bayes classifier, Neural Networks, SVM, clustering and applications with Phyton |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Phyton programming language basic information | | | Week 2 | Phyton programlama dili temel bilgiler | | | Week 3 | KNN classification with Phyton | | | Week 4 | KNN classification with Phyton | | | Week 5 | Linear regression method implementation with Phyton | | | Week 6 | Linear regression method implementation with Phyton | | | Week 7 | Mid-term examination | | | Week 8 | Naive Bayes classification with Phyton | | | Week 9 | Naive Bayes classification with Phyton | | | Week 10 | Implementation of Neural Networks with Phyton | | | Week 11 | Implementation of Neural Networks with Phyton
| | | Week 12 | Implementation of SVM method with Phyton
| | | Week 13 | Implementation of SVM method with Phyton
| | | Week 14 | Clustering with Phyton | | | Week 15 | Clustering with Phyton | | | Week 16 | Final examination | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | | | | | | | |
| 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 | 4 | 14 | 56 | | Sınıf dışı çalışma | 5 | 14 | 70 | | Arasınav için hazırlık | 2 | 6 | 12 | | Arasınav | 2 | 1 | 2 | | Ödev | 3 | 14 | 42 | | Dönem sonu sınavı için hazırlık | 2 | 16 | 32 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 216 |
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