CS3200

Scientific and data computation relevant to computational science and engineering, with emphasis on the process of modeling, simulation, visualization and evaluation.This is an introduction to classical and modern computational and data science methods, with a focus on their theoretical and algorithmic development as well as their implementation. Topics may include numerical linear algebra, numerical approximation methods such as interpolation and linear regression, solving and linear and non-linear systems, and optimization. Basic knowledge of programming, matrices, and calculus is assumed. Recommended programming experience at the level of CS 2420 and Mathematical background at the level of integral calculus.

Prerequisites: 'C-' or better in (MATH 2270 OR MATH 2271) AND ((‘C-‘ or better in (CS 1400 AND CS 1410) OR CS 1420) AND (‘B-‘ or better in CS 2420) AND (‘C’ or better in MATH 1210)) AND Major or Minor in KSoC or ECE or Physics.

CS 2420 AND Integral Calculus.

Spring