Ingrid Russell

An introduction to the WEKA data mining system

This is a proposal for a half day tutorial on Weka, an open source Data Mining software package written in Java and available from www.cs.waikato.ac.nz/~ml/weka/index.html. The goal of the tutorial is to introduce faculty to the package and to the pedagogical possibilities for its use in the undergraduate computer science and engineering curricula. The Weka system provides a rich set of powerful Machine Learning algorithms for Data Mining tasks, some not found in commercial data mining systems. These include basic statistics and visualization tools, as well as tools for pre-processing, classification, and clustering, all available through an easy to use graphical user interface.

Enhancing undergraduate AI courses through machine learning projects

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

Non-traditional projects in the undergraduate AI course

1. SUMMARY Projects in the Artificial Intelligence course have evolved over the years. Along the way, they have taken several forms, including small-scale LISP/Prolog projects, larger-scale object-oriented projects in CLOS/C++, projects organized around games, and more recently, projects organized around the concept of agents. All along, educators have attempted to make the projects more appealing and instructive at the same time.

Pedagogical Possibilities for the N-Puzzle Problem

In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Several hands-on laboratory projects that can be closely integrated into an introductory AI course have been developed. We present an overview of one of the projects and describe the associated curricular materials that have been developed. The project uses machine learning as a theme to unify core AI topics in the context of the N-puzzle game. Games provide a rich framework to introduce students to search fundamentals and other core AI concepts. The paper presents several pedagogical possibilities for the N-puzzle game, the rich challenge it offers, and summarizes our experiences using it

Integrating games and machine learning in the undergraduate computer science classroom

A student will be more likely motivated to pursue a field of study if they encounter relevant and interesting challenges early in their studies. The authors are PIs on two NSF funded course curriculum development projects (CCLI). Each project seeks to provide compelling curricular modules for use in the Computer Science classroom starting as soon as CS 1. In this paper, we describe one curriculum module which is the synergistic result of these two projects. This module provides a series of challenges for undergraduate students by using a game environment to teach machine learning and classic Artificial Intelligence concepts.

CURRENT AND FUTURE TRENDS IN FEATURE SELECTION AND EXTRACTION FOR CLASSIFICATION PROBLEMS

In this article, we describe some of the important currently used methods for solving classification problems, focusing on feature selection and extraction as parts of the overall classification task. We then go on to discuss likely future directions for research in this area, in the context of the other articles from this special issue. We propose that the next major step is the elaboration of a theory of how the methods of selection and extraction interact during the classification process for particular problem domains, along with any learning that may be part of the algorithms. Preferably this theory should be tested on a set of well-established benchmark challenge problems. Using this theory, we will be better able to identify the specific combinations that will achieve best classification performance for new tasks.

ADVANCES IN KNOWLEDGE ACQUISITION AND REPRESENTATION

The articles in this special issue represent advances in several areas of knowledge acquisition and knowledge representation. In this article we attempt to place these advances in the context of a fundamental challenge in AI; namely, the automated acquisition of knowledge from data and the representation of this knowledge to support understanding and reasoning. We observe that while this work does indeed advance the field in important areas, the need exists to integrate these components into an end-to-end system and begin to extract general methodologies for this challenge. At the heart of this integration is the need for performance feedback throughout the process to guide the selection of alternative methods, the support for human interaction in the process, and the definition of general metrics and testbeds to evaluate progress.

Using the arduino platform to enhance student learning experiences

We present preliminary experiences using the Arduino microprocessor platform in the undergraduate computing curricula, at both the upper and lower levels. The goal is to enhance student learning by engaging them in a contextualized project-based learning experience and introducing them to fundamental computing and engineering concepts in the context of a highly visual and easy to use environment.

MLeXAI: A Project-Based Application-Oriented Model

Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that can be closely integrated into a one-term AI course. Each project involves the development of a machine learning system in a specific application. These projects have been used in six different offerings over a three-year period at three different types of institutions. While we have presented a sample of the projects as well as limited preliminary experiences in other venues, this article presents the first assessment of our work over an extended period of three years. Results of assessment show that the projects were well received by the students. By using projects involving real-world applications we provided additional motivation for students. While illustrating core concepts, the projects introduced students to an important area in computer science, machine learning, thus motivating further study.

Implementing the intelligent systems knowledge units of computing curricula 2001

Computing curricula 2001 (CC-2001) presents a set of curricular recommendations for undergraduate computer science programs. CC-2001 presents a computer science body of knowledge and identifies a list of core topics/units within each component body of knowledge that a computer science program should require. While some of these core units span hours that warrant or are equivalent to a full course, the core units for other areas are significantly less. This paper presents our experiences with integrating intelligent systems (IS) core units of CC-2001 into the undergraduate curriculum through the more traditional core courses such as discrete mathematics, data structures, and algorithms, thus eliminating the need to require a full course in the area in departments with various constraints that prevent this from being possible.