- 对机器学习概念的基本理解
- 熟悉 Python 程式设计
观众
- AI 初学者
- 数据科学爱好者
可解释 AI （XAI） 是一个重要领域，专注于使 AI 模型更具可解释性和透明度。本课程向参与者介绍 XAI 的基本概念、技术和实际应用，帮助他们了解透明度在 AI 模型开发中的重要性。
这种由讲师指导的现场培训（在线或现场）面向希望学习可解释 AI 基础知识及其在开发负责任的 AI 系统中的作用的初级专业人员。
在本次培训结束时，参与者将能够：
- 了解可解释的 AI 的基本概念。
- 探索透明度和可解释性在 AI 模型中的重要性。
- 了解使 AI 模型可解释的基本技术。
- 将 XAI 技术应用于简单的机器学习模型。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在即时实验室环境中动手实施。
课程自定义选项
- 要申请本课程的定制培训，请联系我们进行安排。
可解释的 AI 简介
- 什么是可解释的 AI （XAI）？
- AI 模型中透明度的重要性
- AI 可解释性的主要挑战
基本 XAI 技术
- 与模型无关的方法：LIME、SHAP
- 特定于模型的可解释性方法
- 解释黑盒模型做出的决策
使用 XAI 工具动手实践
- 开源 XAI 库简介
- 在简单的机器学习模型中实现 XAI
- 可视化解释和模型行为
可解释性方面的挑战
- 准确性与可解释性权衡
- 当前 XAI 方法的局限性
- 处理可解释模型中的偏差和公平性
XAI 中的道德考虑
- 了解 AI 透明度的道德影响
- 平衡可解释性与模型性能
- XAI 中的隐私和数据保护问题
XAI 的实际应用
- XAI 在医疗保健、金融和执法领域的应用
- 可解释性的法规要求
- 通过透明度建立对 AI 系统的信任
高级 XAI 概念
- 探索反事实解释
- 解释神经网路和深度学习模型
- 解释复杂的 AI 系统
可解释 AI 的未来趋势
- XAI 研究中的新兴技术
- 未来 AI 透明度的挑战和机遇
- XAI 对负责任的 AI 开发的影响
总结和后续步骤
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