We will discuss the following NLA topics
Our main applications include large scale NLA, optimization, and large data analysis. Our course has a significant overlap, but is different from the topics course, Stat260/CompSci294 , "Randomized Algorithms for Matrices and Data", offered by Prof. Michael Mahoney in Fall 2013.
Prof. Ming Gu
Office: Evans 861
Lectures: MF, 11:00AM-12:30PM, 891 Evans Hall.
Office Hours: TBA , or by appointment.
There are no exams nor weekly homeworks. Course work will instead include literature research and presentation in related topics. Additionally, we will have two progamming projects, and one term paper, both to be done in groups of 2-3 students. This course will be structured in such a way that a successful term paper should be one that will be submitted for journal publication. We will discuss the following NLA topics
Basic tools in Numerical Linear Algebra and Probability Theory