Enhancing undergraduate AI courses through machine learning projects

Z. Markov, I. Russell, T. Neller, S. Coleman
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引用次数: 20

Abstract

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
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通过机器学习项目加强本科人工智能课程
人们普遍认为,本科人工智能入门课程的教学具有挑战性。这在一定程度上是由于通常涵盖的核心主题的多样性和看似不相关。本文介绍了由美国国家科学基金会资助的工作,以解决这一问题,并提高学生在课程中的学习体验。我们的工作包括通过统一的机器学习主题开发一个可适应的框架,用于展示核心人工智能主题。开发了一套实践性的学期项目,每个项目都涉及一个学习系统的设计和实现,该系统增强了一个常用部署的应用程序。这些项目使用机器学习作为一个统一的主题,将核心人工智能主题联系在一起。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在其中一个项目中的经验,即我们在AI课程中使用的Web User Profiling
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