June 18, 2006
June 18, 2006
June 21, 2006
Electrical and Computer
11.234.1 - 11.234.19
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.
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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