Data Science II

An introduction to advanced analytics techniques in data science, including random forests, semi-supervised learning, spectral analytics, randomized algorithms, and just-in-time compilers. Distributed and out-of-core processing.

Prerequisites:

http://bulletin.uga.edu/CoursesHome.aspx

Credit Hours:
4
Course Information File:
CIS_CSCI_4360.pdf