| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Explains the fundamental concepts of artificial intelligence and machine learning.
- Defines supervised, unsupervised, and reinforcement learning methods and applies them to examples.
- Applies data preprocessing and feature extraction methods for biomedical data.
- Recognizes deep learning architectures and applies them to biomedical image and signal data.
- Gains knowledge about clinical decision support systems and AI-based biomedical applications.
- Develops simple machine learning applications using Python, MATLAB, or similar software tools.
- Interprets, reports, and presents the results obtained.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | BENG335
BENG226 |
|---|
| Course Description | Within the scope of this course, students will learn the fundamentals of artificial intelligence (AI) and machine learning (ML), acquiring knowledge and skills for applications in the field of biomedical engineering. The course covers the historical development of AI, fundamental algorithms, supervised and unsupervised learning methods, deep learning architectures, biomedical data analysis, image processing, signal processing, and their use in clinical decision support systems. Students will be able to apply machine learning algorithms to solve biomedical problems and will gain practical experience through project-based work. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Artificial Intelligence: Definitions, History, and Importance in Biomedical Engineering |
|
2nd Week |
Introduction to Machine Learning: Fundamental Concepts and Learning Paradigms |
|
3rd Week |
Data Preprocessing, Feature Extraction, and Dimensionality Reduction Techniques |
|
4th Week |
Supervised Learning Algorithms (Regression, Classification) |
|
5th Week |
Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction) |
|
6th Week |
Decision Trees, Random Forests, and Support Vector Machines |
|
7th Week |
Artificial Neural Networks and Introduction to Deep Learning |
|
8th Week |
MIDTERM EXAM |
|
9th Week |
Deep Learning Applications in Image Processing and Medical Image Analysis |
|
10th Week |
Biomedical Signal Processing (EEG, ECG) and ML Applications |
|
11th Week |
Clinical Decision Support Systems and AI in Healthcare |
|
12th Week |
Reinforcement Learning and Its Applications in Robotic Systems |
|
13th Week |
Current Research: AI-Based Diagnosis, Treatment, and Personalized Medicine |
|
14th Week |
Project Presentations and General Course Review |
|
| Recommended or Required Reading | Pattern Recognition and Machine Learning, Christopher Bishop, Springer.
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press.
Biomedical Signal and Image Processing with Artificial Intelligence, Academic kaynaklar.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Presentation
- Project
- Problem Solving
- Discussion
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
2 |
10 |
| Project |
1 |
30 |
| Total(%) |
|
70 |
| Contribution of In-term Studies to Overall Grade(%) |
|
70 |
| Contribution of Final Examination to Overall Grade(%) |
|
30 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1.5 |
1.5 |
| Preparation for Quiz |
2 |
10 |
20 |
| Individual or group work |
14 |
2 |
28 |
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
15 |
15 |
| Laboratory (including preparation) |
14 |
1 |
14 |
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 142.5 |
| Total Work Load / 30 (h) | 4.75 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | English |
|---|
| Work Placement(s) | No |
|---|
