PHYS 302 - Sci. Comp. & Machine Learning

Unit(s): 4

This class introduces to the students a selected set of state-of-the-art scientific computing tools, applicable to nearly all scientific/engineering disciplines, such as data fitting, visualization and animation tools, numerical solutions to partial differential equations, Gaussian Process, Markov Chain Monte Carlo, Neural Networks, parallel computing, and symbolic computation.

Prerequisite: (CS 110 with a minimum grade of C and MATH 211 with a minimum grade of C) or PHYS 301 with a minimum grade of C
College of Arts and Sciences