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Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences
Author(s) -
Zdravko Markov,
Todd W. Neller,
Ingrid Russell
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--14721
Subject(s) - artificial intelligence , computer science , presentation (obstetrics) , suite , applications of artificial intelligence , bridge (graph theory) , project based learning , software engineering , mathematics education , medicine , mathematics , archaeology , radiology , history
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering. In this paper we will present our approach, an overview of the project, and the hands-on laboratory modules. Our preliminary experiences incorporating these modules into our introductory AI course will also be presented.

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