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| TBB6004 | Statistical Learning Methods | 2+2+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Compulsory | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | Doç. Dr. Burçin KURT | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course provides information about the basic concepts and used methods in statistical learning (machine learning) and describes the use of these methods in the field of health sciences (bioinformatics, medical informatics). Thus, students will learn how can they select and apply the appropriate methods in their studies. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Information about the concept of statistical learning. | | 1 | | PO - 2 : | Information on statistical learning methods. | | 1 | | PO - 3 : | Use of statistical learning methods. | | 1 | | PO - 4 : | The use of statistical learning methods in the field of health sciences. | | 1,6 | | PO - 5 : | Select and apply appropriate methods using package programs. | | 1,6 | | PO - 6 : | Sample applications with the help of software packages. | | 1,6 | | 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 | | |
| Statistical Learning about the basic description, Boosting and cross validation, bootstrap methods, properties and applications, different samples of Neural Networks,, Naive Bayes algorithm and conditions of use, Genetic Algorithms, Kohonen clustering method, Image Processing Concepts, Unsupervised Learning-Based Classification Techniques. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | The Basic Concepts of Statistical Learning | | | Week 2 | The Basic Concepts of Statistical Learning | | | Week 3 | Boosting and Cross Validation | | | Week 4 | Bootstrap Methods and Algorithms | | | Week 5 | Artificial Neural Networks-1 | | | Week 6 | Artificial Neural Networks-2 | | | Week 7 | Naive Bayes Method-1 | | | Week 8 | Naive Bayes Method-2 | | | Week 9 | Genetic Algorithms-1 | | | Week 10 | Genetic Algorithms-2 | | | Week 11 | Kohonen Clustering Algorithm and Application
| | | Week 12 | Basic Concepts in Image Processing | | | Week 13 | Microarray Data and Statistical Learning-1
| | | Week 14 | Microarray Data and Statistical Learning-2
| | | Week 15 | Classification Techniques based on Unsupervised Learning | | | Week 16 | Project Presentation and Discussion | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 7 | | 2 | 30 | | In-term studies (second mid-term exam) | 6 | | | 10 | | Practice | 15 | | | 20 | | End-of-term exam | 16 | | 2 | 40 | | |
| 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 | 2 | 16 | 32 | | Arasınav | 2 | 1 | 2 | | Uygulama | 2 | 16 | 32 | | Ödev | 4 | 1 | 4 | | Total work load | | | 70 |
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