Document Type
Conference Material
Publication Date
10-19-2005
Department 1
Computer Science
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.
Copyright Note
This is the publisher's version of the work. This publication appears in Gettysburg College's institutional repository by permission of the copyright owner for personal use, not for redistribution.
Recommended Citation
Russell, Ingrid, et al. "Enhancing Undergraduate AI Courses through Machine Learning Projects." Proceedings of the 35th ASEE/IEEE Frontiers in Education Conference (October 19-22, 2005) Indianapolis, IN.
Required Publisher's Statement
Original version of the conference proceedings is available from the publisher at: http://fie-conference.org/fie2005/index.htm