M693-002 Course Announcement
Spring 2002
Geometric Data Analysis: Theory,
Algorithms and Computing
Description: A time-honored approach for the investigation of unexplained phenomena is to attempt to infer laws, or explain processes, from the patterns present in collected data. However, our phenomenal ability to acquire data has outstripped our ability to analyze it. Thus, researchers today are confronted with a modern dilemma. Presumably the more information available concerning a phenomenon the better. Yet, a massive data set storing the information, in and of itself, a potentially significant barrier to the investigation.
We will present an overview of several mathematical tools for overcoming problems associated with analyzing massive data sets. Our approach is geometric in nature and the main tool is the dimensionality reducing mapping. These mappings are required for the analysis and representation of information (patterns) in large data sets generated by physical or numerical experiments. The main techniques to be presented include optimal orthogonal expansions, Fourier analysis, neural networks and wavelets. We will emphasize the mathematical similarities and differences of the procedures in the context of applications to
Homework: This will consist of computer assignments as well as the mathematical foundations of the subject.
Text: Geometric Data Analysis, Michael Kirby, Wiley & Sons
Registration: To register for this course please sign up for M693 section 2 (3 credits--reference number 286608) as well as the computer lab M695V Section 8 (1 credit--reference number 286482) . This will produce 3+1=4credits. The lab hour will be arranged.
Time and Place: Tuesdays and Thursdays, 2:10--3:25, Engineering B-103
Information:
Professor: Michael Kirby, Phone: 491-6850, Email: kirby@math.colostate.edu
Office Hours: Tuesday 1-2, Wednesday 9:30-10:30, Thursday 10-11, Weber 134.
Data Sets:
Homework: