This seminar will be conducted
in collaboration with Siemens Corporate Research (SCR) is located in
Princeton, NJ. SCR (http://www.scr.siemens.com/) provides Siemens
operating companies with state-of-the-art imaging, software, multimedia
and data analysis technologies. As such, Siemens is concerned with
developing and applying cutting edge techniques in multivariate signal
processing, pattern recognition and learning algorithms. During the
course of this seminar students will investigate "real-world" data sets
provided by SCR that will both illustrate elegant applications of
existing abstract mathematical algorithms and also demonstrate the
constant necessity for customization and
extension.
The topics we will consider for the evaluating of the Siemens data sets will include
Coursework and Assessment: projects will be group orientedand results will be evaluated by the instructor and Siemens. Students using this seminar in partial fulfillment of the MS program in Applied and Computational Mathematics will be required to present a summary of their projects to their Masters committee.
Prerequisites: Knowledge of a programming language, e.g., MATLAB, C++ or Java. Linear Algebra (M369 or M560), Multivariable Calculus (M261)
Recommended Reading: Geometric Data Analysis, M. Kirby; The Elements of Statistical Learning: Data Mining, Inference, and Prediction by T. Hastie, R. Tibshirani, J. H. Friedman; Pattern Classification by Richard O. Duda, Peter E. Hart, David G. Stork.
Instructor: Professor Kirby, Weber 115, kirby@math.colostate.edu