Zdravko Markov
Central Connecticut State University
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technical symposium on computer science education | 2006
Zdravko Markov; Ingrid Russell
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.
frontiers in education conference | 2005
Zdravko Markov; Ingrid Russell; Todd W. Neller; Susan Coleman
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
frontiers in education conference | 2006
Zdravko Markov; Ingrid Russell; Todd W. Neller; Neli P. Zlatareva
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
Proceedings of the 3rd international conference on Game development in computer science education | 2008
Scott A. Wallace; Ingrid Russell; Zdravko Markov
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.
International Journal of Pattern Recognition and Artificial Intelligence | 2005
Lawrence B. Holder; Ingrid Russell; Zdravko Markov; Anthony G. Pipe; Brian Carse
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.
International Journal on Artificial Intelligence Tools | 2006
Lawrence B. Holder; Zdravko Markov; Ingrid Russell
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.
ACM Transactions on Computing Education | 2010
Ingrid Russell; Zdravko Markov; Todd W. Neller; Susan Coleman
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.
technical symposium on computer science education | 2006
Ingrid Russell; Zdravko Markov; Todd W. Neller
An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. It is generally recognized that an introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach [9]. Others have been working to integrate Robotics into the AI course [1, 2, 3].We present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence course around the theme of machine learning. This 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. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning also provides a bridge between AI technology and modern software engineering. In his article, Mitchell discusses the increasingly important role that machine learning plays in the software world and identifies three important areas: data mining, difficult-to-program applications, and customized software applications [6].We have developed a suite of adaptable, hands-on laboratory projects that can be closely integrated into the introductory AI course. Each project involves the design and implementation of a learning system which will enhance a particular commonly-deployed application. The goal is to enhance the student learning experience in the introductory artificial intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the important relationship between AI and computer science in general, and software development in particular. Details on this project as well as samples of course materials developed are published in [4, 5, 7, 8] and are available at the project website at http://uhaweb.hartford.edu/compsci/ccli.We present an overview of our work along with a detailed presentation of one of these projects and how it meets our goals.The project involves the development of a learning system for web document classification. Students investigate the process of classifying hypertext documents, called tagging, and apply machine learning techniques and data mining tools for automatic tagging. Our experiences using the projects are also presented.
Springer US | 2011
Darina Dicheva; Zdravko Markov; Eliza Stefanova
This volume contains the Proceedings of The Third International Conference on Software, Services & Semantic Technologies (S3T) held in Bourgas, Bulgaria on September 1-3, 2011. It is the third S3T conference in a series of annually organized events supported by the F7 EU SISTER Project and hosted by Sofia University. The conference is aimed at providing a forum for researchers and practitioners to discuss the latest developments in the area of Software, Services and Intelligent Content and Semantics. The conference sessions and the contents of this volume are structured according to the conference track themes: Intelligent Content and Semantics (10 papers), Knowledge Management, Business Intelligence and Innovation (4 papers), Software and Services (6 papers), and Technology Enhanced Learning (9 papers). The papers published in this volume cover a wide range of topics related to the track themes. Particular emphasis is placed on applying intelligent semantic technologies in educational and professional environments with papers in the areas of Ontologies and Semantic Web Technologies, Web Data and Knowledge, Social Networks Analysis, Information Extraction and Visualisation, Semantic Search and Retrieval, E-learning, and User Modelling and Personalization.
european conference on machine learning | 2000
Zdravko Markov; Ivo Marinchev
In the present paper we propose a consistent way to integrate syntactical least general generalizations (lggs) with semantic evaluation of the hypotheses. For this purpose we use two different relations on the hypothesis space - a constructive one, used to generate lggs and a semantic one giving the coverage-based evaluation of the lgg. These two relations jointly implement a semantic distance measure. The formal background for this is a height-based definition of a semi-distance in a join semi-lattice. We use some basic results from lattice theory and introduce a family of language independent coverage-based height functions. The theoretical results are illustrated by examples of solving some basic inductive learning tasks.