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MLDS 490: Explainable AI


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Description

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, various explanation methods, and how to evaluate and apply XAI in real-world scenarios. 

Upon completion of the course, students will be able to:

  • Understand the importance of XAI and its role in building trust in AI systems. 
  • Describe and apply various XAI techniques, including model-specific and model-agnostic methods. 
  • Evaluate the quality of explanations and choose appropriate methods for different applications. 
  • Implement XAI techniques using Python, R, and relevant libraries. 
  • Analyze case studies.