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.
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.
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.
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