Assessing And Evaluating Our Crcd Experiences At The University Of Central Florida: An Nsf Project
- 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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19
- Page Numbers
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11.234.1 - 11.234.19
- DOI
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10.18260/1-2--763
- Permanent URL
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https://peer.asee.org/763
- Download Count
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447
Paper Authors
Michael Georgiopoulos University of Central Florida
MICHAEL GEORGIOPOULOS is a Professor at the School of EECS at the University of Central Florida. His research interests lie in the area of neural networks and applications. He is an Associate Editor of the IEEE Transcations on Neural Networks and the Neural Networks journal
Erol Gelenbe Imperial College
EROL GELENBE is a Professor at the Imperial College in London, and a Research Professor at the University of Central Florida. He is a Fellow of IEEE and a Fellow of ACM. His research interests cover packet network design, computer performance analysis, artificial neural networks and simulation with enhanced reality.
Ronald DeMara University of Central Florida
RONALD DEMARA is an Associate Professor at the School of Electrical Engineering and Computer Science at the University of Central Florida. He has been a reviewer for National Science Foundation, Journal of Parallel and Distributed Computing, IEEE Transactions on Parallel and Distributed Computing. His interests lie in the areas of Parallel and distributed processing, self-timed architectures.
Avelino Gonzalez University of Central Florida
AVELINO GONZALEZ is a Professor of the School of Electrical Engineering and Computer Science at the University of Central Florida. He has co-authored a book entitled, “The Engineering of Knowledge-Based Systems: Theory and Practice”. His research interests lie in the areas of artificial intelligence, context based behavior and representation, temporal reasoning, intelligent diagnostics and expert systems.
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.
Mansooreh Mollaghasemi University of Central Florida
MANSOOREH MOLLAGHASEMI is an Associate Professor at the Industrial Engineering and Management Sciences (IEMS) Department at the University of Central Florida. She has co-authored three books in the area of Multiple Objective Decision Making. Her research interests lie in Simulation Modeling and Analysis, Optimization, Multiple Criteria Decision Making, Neural Networks and Scheduling.
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
Georgios Anagnostopoulos Florida Tech
GEORGIOS ANAGNOSTOPOULOS is an Assistant Professor in the Department of Electrical and Computer Engineering at the Florida Institute of Technology. His research interests lie in the areas of Neural Networks, Machine Learning and Pattern Recognition.
Ingrid Russell
INGRID RUSSELL is a Professor of Computer Science at the University of Hartford. Her research interests are in the areas of artificial neural networks, pattern recognition, semantic web technologies, and computer science education. She has been involved in several computer science curriculum projects. Most recently she chaired the Intelligent Systems focus group of the IEEE-CS/ACM Task Force on Computing Curricula 2001.
Jimmy Secretan University of Central Florida
JIMMY SECRETAN is a Ph.D. student in the Department of Electrical and Computer Engineering at the University of Central Florida. His research interests lie in the areas of Machine Learning and cluster computing.
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Assessing and Evaluating our progress on the CRCD Experiences at the University of Central Florida: An NSF Project Abstract
Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded to other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have any exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National Science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. In the past two years, we have reported on our experiences of introducing Machine Learning modules in sophomore and junior undergraduate classes, as well as our experiences of teaching two senior level Machine Learning classes, entitled Machine Learning I and Machine Learning II. In Machine Learning I we introduce our research to the students in the class. In Machine Learning II we assign research projects to the students and we help them produce their own contributions in the Machine Learning field. One important component of our project is the assessment and evaluation of our efforts. Last spring (spring of 2005) we have invited a CRCD Advisory Board consisting of academicians, and government/industry professionals, with expertise in Machine Learning, to a 1-day CRCD Symposium at the University of Central Florida to assess and evaluate the CRCD experience. This paper reports the results of the CRCD Assessment and Evaluation conducted by the CRCD Board.
1. Introduction
In the last decade there has been an explosion of research in machine learning. A contributing factor is that traditionally independent research communities in symbolic machine learning, computational learning theory, neural networks, genetic algorithms, statistics, and pattern recognition have achieved new levels of collaboration. The outcome has been a plethora of results in machine learning emerging from all of these research communities working synergistically. The second reason for the explosive growth is that machine learning has been applied successfully to a growing range of problems in science and engineering, such as speech recognition, handwritten recognition, medical data analysis, game playing, knowledge data discovery in databases, language processing, robot control, and others.
Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering and mechanical engineering. Currently, quite a few undergraduate and first-year graduate stud nets in the aforementioned fields do not have access to coursework and exposure to recent research trends in Machine Learning. The effort in this CRCD project is attempting to remedy these shortcomings. By involving in this CRCD effort a strong team of
Georgiopoulos, M., & Gelenbe, E., & DeMara, R., & Gonzalez, A., & Kysilka, M., & Mollaghasemi, M., & Wu, A., & Anagnostopoulos, G., & Russell, I., & Secretan, J. (2006, June), Assessing And Evaluating Our Crcd Experiences At The University Of Central Florida: An Nsf Project Paper presented at 2006 Annual Conference & Exposition, Chicago, Illinois. 10.18260/1-2--763
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