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Information
| Unit | INSTITUTE OF MEDICAL SCIENCES |
| BIOSTATISTICS (MEDICINE) (MASTER) (WITHOUT THESIS) (EVENING EDUCATION) | |
| Code | BIST529 |
| Name | Machine Learning and Image Processing |
| Term | 2025-2026 Academic Year |
| Term | Spring |
| Duration (T+A) | 2-2 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisansüstü Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. İLKER ÜNAL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to enable students to understand basic machine learning and image processing methods used in health sciences, recognize appropriate application areas, and interpret results from a clinical decision-support perspective.
Course Content
This course covers machine learning approaches and image processing techniques used in health sciences. Topics include supervised and unsupervised learning, feature extraction, model training and validation, performance metrics; preprocessing, segmentation, classification, and basic deep learning concepts applied to medical imaging data (radiology, pathology, dermatology, etc.). Applications use health-specific datasets with an emphasis on clinical interpretation of results.
Course Precondition
No prerequisites. (Basic knowledge of statistics and computer usage is recommended.)
Resources
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R.
Notes
Recent machine learning and medical imaging research articles
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain machine learning and image processing concepts used in health sciences. |
| LO02 | Distinguish between supervised and unsupervised learning approaches. |
| LO03 | Apply basic preprocessing and feature extraction steps to medical images. |
| LO04 | Perform classification/segmentation applications using simple machine learning models. |
| LO05 | Evaluate model performance using appropriate metrics. |
| LO06 | Interpret machine learning results within a clinical context. |
| LO07 | Observe ethical, privacy, and data security principles when working with health data. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain basic biostatistics, probability, and demographic concepts used in health sciences. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Define research designs, sampling methods, and data types used in health research. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explain the foundations of statistical approaches used in healthcare decision-making processes. | 2 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Explain the basic logic of regression, modeling, and advanced statistical methods used in health sciences. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Analyze and interpret data obtained from health research using appropriate statistical methods. | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Perform statistical analyses and generate outputs using statistical software packages. | 2 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Apply basic data science, artificial intelligence, and machine learning applications in health sciences. | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Evaluate multiple regression and survival analysis results in a clinical context. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Analyze genetic and biomedical data using basic analytical approaches. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Apply scale development, validity, and reliability analyses. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Conduct data analysis and reporting within the scope of a term project. | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Apply and manage sampling procedures in health studies. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Critically evaluate scientific studies from a statistical perspective. | 1 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Present analysis results in accordance with ethical principles. | 2 |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | Applies fundamental concepts of epidemiology and health statistics to clinical and field settings. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Course introduction; introduction to machine learning in healthcare | Reading | Öğretim Yöntemleri: Anlatım |
| 2 | Types of machine learning and basic concepts | Reading | Öğretim Yöntemleri: Anlatım |
| 3 | Data preparation and feature extraction | Reading | Öğretim Yöntemleri: Anlatım |
| 4 | Supervised learning: classification | Reading | Öğretim Yöntemleri: Anlatım |
| 5 | Unsupervised learning: clustering | Reading | Öğretim Yöntemleri: Anlatım |
| 6 | Model training and validation | Reading | Öğretim Yöntemleri: Anlatım |
| 7 | Performance metrics | Reading | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Sözlü Sınav |
|
| 9 | Introduction to medical imaging data | Reading | Öğretim Yöntemleri: Anlatım |
| 10 | Image preprocessing techniques | Reading | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Image segmentation | Reading | Öğretim Yöntemleri: Anlatım |
| 12 | Basic deep learning approaches | Reading | Öğretim Yöntemleri: Anlatım |
| 13 | Applied examples in health sciences I | Reading | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 14 | Applied examples in health sciences II | Reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 15 | General review and evaluation of applications | Reading | Öğretim Yöntemleri: Soru-Cevap, Tartışma |
| 16 | Term Exams | Ölçme Yöntemleri: Proje / Tasarım, Ödev |
|
| 17 | Term Exams | Ölçme Yöntemleri: Ödev, Proje / Tasarım |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||