| Language of Instruction |
English |
| Course Type |
Elective |
| Course Level |
Masters Degree |
| Course Instructor(s) |
Pınar ULUER
puluer@gsu.edu.tr (Email)
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| Assistant |
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| Objective |
This course focuses on explaining and interpreting the decisions of machine learning algorithms. The course primarily aims to introduce students to explainable artificial intelligence (XAI) methods and demonstrate, through practical applications, how these methods are used in various areas.
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| Content |
This course aims to interpret the decisions, predictions, or inferences of AI-based systems, and to explain how and why these outputs are calculated by existing algorithms. The course provides an overview of interpreting the decisions of artificial learning models used in various fields, from healthcare to finance, often referred to as "black boxes," and the critical aspects of developing reliable, transparent, and ethically compliant AI systems. Students will have the opportunity to apply the methods described in the course using Python and discuss their results.
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| Course Learning Outcomes |
After completing the course, the student:
1- can explain the core concepts of XAI, and the difference between interpretability and explainability,
2- can justify the selection of explainability methods by critically evaluating their theoretical foundations, underlying algorithms, and inherent strengths and limitations.
3- is prepared to do research on the topic of XAI, including designing and implementing appropriate methods and algorithms,
4- is able to implement XAI methods and critically evaluate them based on the specific data and problem being addressed.
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| Teaching and Learning Methods |
Theory and practice, presentation, discussion, question-answer
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| References |
- Mehta, M., Palade, V., & Chatterjee, I. (Eds.). (2023). Explainable AI: Foundations, methodologies and applications (Vol. 232, p. 273). Springer.
- Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K. R. (Eds.). (2019). Explainable AI: interpreting, explaining and visualizing deep learning (Vol. 11700). Springer Nature.
- Molnar, C. (2020). Interpretable machine learning.
- Hsieh, W., Bi, Z., Jiang, C., Liu, J., Peng, B., Zhang, S., ... & Liu, M. (2024). A comprehensive guide to explainable AI: from classical models to LLMs. arXiv preprint arXiv:2412.00800.
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