Name: Explainable Machine Learning (XAI)
Brand: Duke University
Availability: OnlineOnly
Rating: 4.7 (5 reviews)

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学生对 Duke University 提供的 Explainable Machine Learning (XAI) 的评价和反馈

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课程概述

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....

1 - Explainable Machine Learning (XAI) 的 5 个评论（共 5 个）

创建者 Leonardo F D G

•

Oct 24, 2025

Like the previous courses in this specialization, this course delivers a strong balance of theory and practical, hands-on experience, helping us better understand how explainable ML is applied across real-world domains.