Engaging Undergraduate Students In Machine Learning Research: Progress, Experiences And Achievements Of Project Emd Mlr
- Conference
- Location
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Chicago, Illinois
- Publication Date
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June 18, 2006
- Start Date
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June 18, 2006
- End Date
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June 21, 2006
- ISSN
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2153-5965
- Conference Session
- Tagged Division
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Electrical and Computer
- Page Count
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13
- Page Numbers
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11.544.1 - 11.544.13
- DOI
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10.18260/1-2--1150
- Permanent URL
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https://peer.asee.org/1150
- Download Count
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471
Paper Authors
Georgios Anagnostopoulos Florida Tech
GEORGIOS C. ANAGNOSTOPOULOS is an Assistant Professor in the Electrical & Computer Engineering department of Florida Institute of Technology. His research interests are statistical machine learning, neural networks and data mining.
Michael Georgiopoulos University of Central Florida
MICHAEL GEORGIOPOULOS is a Professor of the Department of Electrical and Computer Engineering at the University of Central Florida. His research interests lie in the areas of neural networks and applications of neural networks in pattern recognition, image processing, smart antennas and data-mining. He is an Associate Editor of the IEEE Transactions on Neural Networks since 2001.
Ken Ports Florida Tech
KEN PORTS is a Professor of the Department of Electrical and Computer Engineering at Florida Tech. He is also the Engineering Director of Florida TechStart, the university business accelerator. His interests include microelectronics, nanoelectronics and radiation effects, entrepreneurial behavior and culture, and business processes such as product to market, strategic planning and execution, and project management. Dr. Ports has 48 publications and 11 patents.
Richie Samuel University of Central Florida
SAMUEL RICHIE is an Associate Professor of the Department of Electrical and Computer Engineering at the University of Central Florida. His research interests include surface wave device modeling, optical character recognition, video image processing, and biomedical instrumentation. He is serving as the Assistant Dean for Distributed Learning for the College of Engineering and Computer Science with operations including nine origination sites serving over 200 courses in engineering per year.
Melinda White Seminole Community College
MELINDA WHITE is a Professor at Seminole Community College in Sanford, FL. She teaches classes in computer programming and applications. Melinda has a Bachelor's degree in Mathematics and a Master's in Instructional Computing, both from the University of Florida. In addition to teaching for over 15 years, she has over 10 years of industry experience in Computer Programming and Systems Analysis.
Veton Kepuska Florida Tech
VETON KËPUSKA is an Associate Professor of the Electrical and Computer Engineering Department at the Florida Institute of Technology. He has joined academia after over a decade of R&D work in high-tech Speech Recognition Industry in Boston area. His research interests lie in the areas of Speech Processing and Recognition, Speech Coding, Microphone Arrays, Neural Networks and Applications of Neural Networks in Pattern Recognition, Speech Processing and Recognition, Blind Source Separation, , Image Processing, Natural Language Understanding, Human Machine Interface. He holds a number of patents in speech recognition area.
Philip Chan Florida Tech
PHILIP CHAN is an Associate Professor of Computer Science at Florida Institute of Technology. His main research interests include scalable adaptive methods, machine learning, data mining, distributed and parallel computing, and intelligent systems. He received his PhD, MS, and BS in Computer Science from Columbia University, Vanderbilt University, and Southwest Texas State University respectively. He is an Associate Editor for the Knowledge and Information Systems journal.
Annie Wu University of Central Florida
ANNIE WU is an Assistant Professor at the School of Electrical Engineering and Computer Science at the University of Central Florida. Her research interests are in the areas of genetic algorithms, machine learning, biological modeling, and visualization.
Marcella Kysilka University of Central Florida
MARCELLA KYSILKA is a Professor and Assistant Chair of the Education Foundations Department at the University of Central Florida. She is active in her professional organizations and currently serves as Associate Editor of the "Journal of Curriculum and Supervision" (the scholarly journal of the Association for Supervision and Curriculum Development). Her research interests are in curriculum studies.
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Engaging Undergraduate Students in Machine Learning Research: Progress, Experiences and Achievements of Project EMD-MLR
Abstract
Project EMD-MLR is a National Science Foundation funded, on-going effort that aims at engaging undergraduate engineering and computer science students into research on Machine Learning. In our present paper we will provide a brief overview of the project’s characteristics and share our experience about engaging undergraduate students in research in year 1 of the project. More specifically, we will report the overall achievements accomplished so far in terms of research products such as student-developed software, publications and other dissemination efforts. Additionally, we report on student assessment results regarding the quality of their experience through their participation in aspects such as the student-teacher interaction, the knowledge and experience that students acquired, while performing research and the type of impact their involvement had on their future academic and/or career aspirations.
1. Introduction
Machine Learning (ML) is a discipline that started evolving as early as the 60’s in the form of Artificial Intelligence and that nowadays has permeated several aspects of high-tech applications as well as everyday life. Its charter is to study, develop and build models able to perform “intelligent” tasks that may be second nature for humans, but are well beyond the capabilities of traditional computing paradigms. ML applications such as vending machines that recognize valid paper bills, document processing software that corrects our grammar and syntax in real time, voice-driven over-the-phone account management of credit, smart photographic cameras that automatically adjust their exposure and speed settings depending on the scene environment, as well as challenging strategy computer games, have become a big part of our everyday routine. Other, less obvious, applications, such as automatic target recognition, earthquake prediction, gene expression discovery, intelligent credit fraud protection and affectionate computing, to mention just a few, are examples of cutting-edge applications of ML in various technological, scientific and financial domains.
This paper describes the outcomes of a prototype project titled “PROJECT EMD-MLR: Educational Materials Development through the Integration of Machine Learning Research into Senior Design Projects“, whose intellectual focus is ML. The project is an on-going, multi- institute effort that started in May 2004. The project partners are two major universities in Central Florida, namely Florida Institute of Technology (FIT) in Melbourne and the University of Central Florida (UCF) in Orlando. In addition to the two host universities, there are two 2-year Central Florida colleges, Seminole Community College (SCC) in Oviedo and Brevard Community College (BCC) in Melbourne. Project EMD-MLR is a National Science Foundation funded project under NSF grant CCLI-0341601 for the period of May 2003 to Arpil 2006 and under the auspices of the Educational Materials Development track of the Course, Curriculum and Laboratories Improvement (CCLI-EMD) program.
Anagnostopoulos, G., & Georgiopoulos, M., & Ports, K., & Samuel, R., & White, M., & Kepuska, V., & Chan, P., & Wu, A., & Kysilka, M. (2006, June), Engaging Undergraduate Students In Machine Learning Research: Progress, Experiences And Achievements Of Project Emd Mlr Paper presented at 2006 Annual Conference & Exposition, Chicago, Illinois. 10.18260/1-2--1150
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