Neural Networks Course Outline
Dr. Yue Wang (Joseph)
Phone: (703) 387-6056
Fax: (703) 528-5543
This course will cover supervised learning, pattern classification, neural networks, support vector machine, unsupervised learning, cluster analysis, feature discovery, cross-validation, and independent component analysis, with an emphasis on the strategic frontier between machine learning and scientific discoveries. The course is theoretical, practical, and challenging in nature. Major effort will be made to present applications in the areas of genomic bioinformatics, computational biology, biomedical imaging, and computer-aided decision making.
Lectures: Tuesdays, 4:00pm – 6:45pm (Blacksburg: check with official schedule)
Office Hours: Tuesdays, 6:45pm- 7:30pm (e-mails are greatly welcome)
Textbook: T Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2001.
References: Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, Inc., 1999.
Hyvarinen, A., Karhunen, J. & Oja, E. Independent Component Analysis, John Wiley, New York, 2001.
Ripley, B. Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
Source: Research Methods Consortium
Neural Networks: Computers With Intuition
Book (World Scientific Pub Co Inc)
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