This paper describes some of the research projects, facilitating machine learning, completed by graduate students supported by the NSF-CRCD AWARD # 9980296 entitled "Machine Learning: A Multidisciplinary Computer Engineering Graduate Program" to Texas Tech University. The program is now under development in parallel at Texas Tech and The University of Missouri at Rolla. As part of the curriculum development, courses were taught in adaptive optimization for signal processing, optimization in information theory and coding, adaptive pattern recognition, neural networks and adaptive critics, and mathematical methods and algorithms for signal processing. Thirty-five graduate students and twelve undergraduate students were significantly involved in both the research and educational activities associated with the program. Research activities were wide-ranging, and included optimized design of lossless and lossy compression for medical images, adaptive pattern recognition, segmentation, adaptive critic designs, Q-learning, optimized blind source separation, fuzzy modeling, and vector quantization. Three specific doctoral level projects involving optimization methods in signal/image processing leading to machine learning have been chosen for this paper since these projects included additional students at Master's and senior undergraduate levels in Electrical and Computer Engineering demonstrating a successful pyramid learning structure using a top down approach. Significant collaboration with federal laboratories, industries, and other universities were developed during the design and development of the projects described in the paper.
|Number of pages||10|
|Journal||ASEE Annual Conference Proceedings|
|State||Published - 2002|
|Event||2002 ASEE Annual Conference and Exposition: Vive L'ingenieur - Montreal, Que., Canada|
Duration: Jun 16 2002 → Jun 19 2002