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Enhancing Undergraduate AI Courses through Machine Learning Projects

机译：通过机器学习项目加强本科均衡课程

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It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects -Web User Profiling which we have used in our AI class.
机译：普遍认为，本科介绍性人工智能课程挑战教学。部分是由于通常覆盖的多样化和看似断开的核心主题。本文提出了国家科学基金会资助的工作来解决这一问题，并在课程中提升学生学习经验。我们的工作涉及通过机器学习的统一主题开发适应核心AI主题的适应框架。开发了一套实践的学期，每个项目都涉及设计和实施学习系统，增强了一个共同部署的应用程序。该项目使用机器学习作为统一主题，以将核心AI主题联接在一起。在本文中，我们将首先提供我们的模型和正在开发的项目的概述，然后将详细介绍我们与我们在我们的AI级别中使用的项目中的一个项目的经验。

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