Time / Location: M W 1:30pm - 2:45pm; 1108 SAS Hall
Course Web Site: http://www.stat.ncsu.edu/people/chi/courses/ST790/
Instructor: Eric Chi
TA: Yuan (Brian) Feng
Prerequisite: ST 758 or equivalent experience. Students should be fluent in at least one of R, MATLAB, Python, or Julia. Students should also have a solid foundation in real analysis, linear algebra, and multivariate calculus.
Texts and Resources (optional):
This course will cover selected topics in statistical computing, with special attention to current research and recent developments in statistics and machine learning. The course is going to be a mix of practice (i.e. programming) and theory (e.g. KKT conditions, proving convergence of iterative methods, etc.) of numerical optimization as applied to statistical estimation.
Throughout the course students will implement algorithms in R and add them into a personal R package.
The final project is intended to serve as a conference paper submission and/or the contents of a dissertation chapter.
Course materials, including lecture notes, homework, and the discussion forum, will be available at https://piazza.com/ncsu/spring2017/st790.