Information Package / Course Catalogue
| Course Code | : MCS512 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 8 |
This course aims to provide computer engineering graduate students with a basic knowledge of signal processing and explainable artificial intelligence. Students will explore the design and application of explainable artificial intelligence algorithms by learning the basic properties and processing methods of signals. The course aims to develop practical application skills as well as theoretical foundations.
This course aims to teach the principles of signal processing and explainable artificial intelligence techniques to computer engineering graduate students. The course will cover the basic properties, analysis and processing of signals, and then examine the use of explainable artificial intelligence techniques. Students will understand key concepts in signal processing and explainable AI, reinforce these concepts with hands-on projects and examples, and develop skills to solve real-world problems using advanced signal processing and explainable AI technique
| Assoc. Prof. Ahmet Çağdaş SEÇKİN |
| Assoc. Prof. Fatih SOYGAZİ |
| 1. | To understand the principles of signal processing and basic concepts. |
| 2. | To be able to use the necessary mathematical tools to analyze and process signals. |
| 3. | To be able to solve statistical decision making problems. |
| 4. | Understanding and applying machine learning and deep learning techniques. |
| 5. | To understand the concept of explainable artificial intelligence and to be able to use explainability methods. |
| 6. | To be able to apply signal processing and explainable artificial intelligence techniques to real world problems. |
| 7. | Practice in signal processing and explainable artificial intelligence through projects and examples |
| 1. | Little, M. A. (2019). Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics. United Kingdom: Oxford University Press. |
| 2. | Rothman, D. (2020). Hands-On Explainable AI (XAI) with Python: Interpret, Visualize, Explain, and Integrate Reliable AI for Fair, Secure, and Trustworthy AI Apps. India: Packt Publishing. |
| 3. | Subasi, A. (2019). Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach. United Kingdom: Elsevier Science. |
| 4. | Chen, T. T. (2023). Explainable Artificial Intelligence (XAI) in Manufacturing: Methodology, Tools, and Applications. Germany: Springer International Publishing. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %40 |
| Project | 1 | %30 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 5 | 3 | 112 |
| Project | 1 | 45 | 3 | 48 |
| Midterm Examination | 1 | 10 | 3 | 13 |
| Final Examination | 1 | 20 | 3 | 23 |
| TOTAL WORKLOAD (hours) | 196 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | |
OÇ-1 | 3 | 4 | 5 | 3 | 4 | 5 | 3 | 4 | 5 |
OÇ-2 | 3 | 4 | 5 | 3 | 4 | 5 | 3 | 4 | 5 |
OÇ-3 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
OÇ-4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
OÇ-5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
OÇ-6 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 |
OÇ-7 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 5 | 4 |