Back to Explainable Machine Learning (XAI)
Learner Reviews & Feedback for Explainable Machine Learning (XAI) by Duke University
10 ratings
About the Course
As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Explainable Machine Learning (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles.
Through discussions, case studies, programming labs, and real-world examples, you will gain the following skills:
1. Implement local explainable techniques like LIME, SHAP, and ICE plots using Python.
2. Implement global explainable techniques such as Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots in Python.
3. Apply example-based explanation techniques to explain machine learning models using Python.
4. Visualize and explain neural network models using SOTA techniques in Python.
5. Critically evaluate interpretable attention and saliency methods for transformer model explanations.
6. Explore emerging approaches to explainability for large language models (LLMs) and generative computer vision models.
This course is ideal for data scientists or machine learning engineers who have a firm grasp of machine learning but have had little exposure to XAI concepts. By mastering XAI approaches, you'll be equipped to create AI solutions that are not only powerful but also interpretable, ethical, and trustworthy, solving critical challenges in domains like healthcare, finance, and criminal justice.
To succeed in this course, you should have an intermediate understanding of machine learning concepts like supervised learning and neural networks.
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1 - 3 of 3 Reviews for Explainable Machine Learning (XAI)
By Peter P
•Jan 17, 2025
If you are going to use AI in your organization, this course is essential.
By Paul R
•Feb 16, 2025
Great! I love how they showed the cuttting edge of research.
By Jorge A R U
•Feb 20, 2025
Nice review of existing methods. Nice labs. However, lecturer does not explain concepts, is just like reading a book.