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所在平台: Udemy |
课程主页: https://www.udemy.com/course/xai-explain-ml-models/
课程评论：没有评论
**课程名称：** XAI：可解释人工智能 **课程概述：** 随着机器学习和人工智能的日益普及，用户对其效用和可用性仍存疑虑。本课程旨在探讨如何以及何时信任这些模型，并解答诸如“为何模型会拒绝某位贷款申请者”、“能提供哪些关于模型或其行为的解释”、“‘解释模型’究竟意味着什么”等关键问题。 课程将介绍人工智能可解释性（XAI）的理论方法，并通过 Python 实践，帮助学员建立对各种可解释性技术的深入理解。我们将从 XAI 方法的整体概览出发，深入探讨不同类型的解释：视觉解释，解释模型的整体行为（全局解释），以及解释模型如何对每一个单独预测做出决策（局部解释）。 学员将有机会将每种方法应用于回归和/或分类任务，并通过动手实践的作业进一步巩固所学技术。 **学习目标：** * 掌握当前最先进的 XAI 方法。 * 理解各种 XAI 方法的优势和局限性。 * 能够将所学工具应用于个人用例和项目中。 **核心内容：** * 机器学习和人工智能的可解释性问题。 * XAI 的理论基础和方法。 * 不同类型的 XAI 解释：视觉、全局和局部解释。 * 使用 Python 实现各种 XAI 技术。 * 在实践中应用 XAI 方法于回归和分类任务。 **课程优势：** XAI 是一个快速发展的研究领域，本课程将帮助学员在这个重要领域打下坚实的基础，为未来在机器学习和人工智能领域的应用做好准备。
Machine learning models are becoming more and more popular. But not every user is convinced in their utility and usability. How and when can we trust the models? If our model has rejected a loan applicant, can we explain to them why that is the case? What types of explanations about the model or its behavior can we provide? What does it even means to explain a model?We address these and other questions in this course on Machine learning or AI explainability (also called XAI in short). We will introduce theoretical approaches and build a hands-on understanding of various explainability techniques in Python.The course builds an overview of XAI approaches before going into details of different types of explanations: visual, explanations of the overall model behavior (so-called global), as well as of how the model reached its decision for every single prediction(so-called local explanations). We will apply each presented approach to a regression and/or classification task; and you will gain ever more practice with the techniques using the hands on assignments.By the end of the course, you should have an understanding of the current state-of-the-art XAI approaches, their benefits and pitfalls. You will also be able to use the tools learned here in your own use cases and projects.XAI is a rapidly developing research field with many open-ended questions. But one thing is certain: it is not going anywhere, the same way Machine learning and AI are here to stay.
