MA 493
The Mathematics of Ranking and Clustering
Spring 2011
Instructors: Carl Meyer and Chuck Wessell
The results of ranking algorithms can have far-reaching consequences, from the bowl game a college football qualifies for to the placement of a web page on a search engine's result page. Data clustering, that is finding patterns in large, multi-dimensional data sets, can be the key to identifying genes associated with a disease, diagnosing cancer patients, finding patterns in different voting districts, or determining when your credit card is being used fraudulently. This course will cover the mathematics behind these two fast-growing areas. In examining ranking, we will survey many of the methods that have been developed in the last century and examine how each can be tailored to specific applications. In the clustering portion of the class, we will again survey the history of the discipline, before concentrating on more recent developments in the field including nonnegative matrix factorization and consensus techniques.
Prerequisites: Linear Algebra (MA 305, MA 405 or equivalent), programming experience (MATLAB will be used in class lectures)
Grading: Homework and Programming Projects
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