ENGR 302 - Scientific Computation

Unit(s): 2

This course is an introduction to powerful scientific computation tools, including rudimentary machine learning techniques. It covers the following topics: numerical differentiation and integration, relaxation method, visualization techniques, Monte Carlo simulations, Discrete Fourier Transform (with applications to image and audio signal processing), Principal Component Analysis (dimensionality reduction for massive machine learning problems), Support Vector Machine (effective for hand-written digit and facial recognition and other non-linear classification problems). Examples and homework problems are readily relatable to students in science and engineering disciplines, and the methods are widely used in biology, chemistry, computer science, engineering, physics and astronomy.

Prerequisite: ENGR 202 and MATH 211
Corequisite: PHYS-371
Restriction: Field of Study restricted to Physics, Engineering Major, Physics, Engineering Physics Minor
College of Arts and Sciences