Fall 2019 Courses

ISC 3222
Symbolic and Numerical Computations
M W F 10:10-11:00 152 DSL
Alan Lemmon
Introduces state-of-the-art software environments for solving scientific and engineering problems. Topics include solving simple problems in algebra and calculus; 2-D and 3-D graphics; non-linear function fitting and root finding; basic procedural programming; methods for finding numerical solutions to DE's with applications to chemistry, biology, physics, and engineering. Prerequisite: MAC 2311.
ISC 3313
Introduction to Scientific Computing with C++
M W F 9:05-9:55 152 DSL
This course introduces the student to the science of computations. Topics cover algorithms for standard problems in computational science, as well as the basics of an object-oriented programming language, to facilitate the student’s implementation of algorithms. The computer language will be C++. Prerequisite: MAC 2311.
DIG 3725/ISC 5326
Introduction to Game and Simulator Design
T R 11:00-12:15 499 DSL
Gordon Erlebacher
Techniques used to design and implement computer games and/or simulation environments. Topics include a historic overview of computer games and simulators, game documents, description/use of a game engine, practical modeling of objects and terrain, use of audio. Physics and artificial intelligence in games covered briefly. Programming is based on a scripting language. Topics are assimilated through the design of a 3D game. Prerequisite: MAC 2311.
ISC 4221C
Algorithms for Science Applications II
M W F 12:20-1:10 152 DSL W 2:30-5:00 (Lab) 152 DSL
Dennis Slice
This course offers stochastic algorithms, linear programming, optimization techniques, clustering and feature extraction presented in the context of science problems. The laboratory component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results. Prerequisites: MAC 2312, ISC 3222. Co-requisite: ISC 4304C.
ISC 4223C
Computational Methods for Discrete Problems
M W F 11:15-12:05 152 DSL M 2:30-5:00 (Lab) 152 DSL
Anke Meyer-Baese
This course describes several discrete problems arising in science applications, a survey of methods and tools for solving the problems on computers, and detailed studies of methods and their use in science and engineering. The laboratory component illustrates the concepts learned in the context of science problems. Prerequisites: MAS 3105, ISC 4304.
ISC 4232C
Computational Methods for Continuous Problems
T R 9:30-10:45 152 DSL T 3:30-6:00 (Lab) 152 DSL
Bryan Quaife
This course provides numerical discretization of differential equations and implementation for case studies drawn from several science areas. Finite difference, finite element, and spectral methods are introduced and standard software packages used. The lab component illustrates the concepts learned on a variety of application problems. Prerequisites: MAS 3105, ISC 4304.
ISC 4933/ISC 5228
Markov Chain Monte Carlo Simulations
M W F 10:10-11:00 499 DSL
Sachin Shanbhag
Covered are statistical foundations of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) simulations, applications of MC and MCMC simulations, which may range from social sciences to statistical physics models, statistical analysis of autocorrelated MCMC data, and parallel computing for MCMC simulations.
ISC 5305
Scientific Programming
T R 9:30-10:45 422 DSL
Xiaoqiang Wang
This course uses the C language to present object-oriented coding, data structures, and parallel computing for scientific programming. Discussion of class hierarchies, pointers, function and operator overloading, and portability. Examples include computational grids and multidimensional arrays.
ISC 5315
Applied Computational Science I
T R 12:30-1:45 152 DSL R 3:30-6:00 (Lab) 152 DSL"
Chen Huang
Course provides students with high-performance computational tools necessary to investigate problems arising in science and engineering, with an emphasis on combining them to accomplish more complex tasks. A combination of course work and lab work provides the proper blend of theory and practice with problems culled from the applied sciences. Topics include numerical solutions to ODEs and PDEs, data handling, interpolation and approximation and visualization. Prerequisites: ISC 5305, MAP 2302.
CAP 5771/ISC 4245C
Data Mining
M W 1:25-2:15 499 DSL
Anke Meyer-Baese
This course enables students to study concepts and techniques of data mining, including characterization and comparison, association rules mining, classification and prediction, cluster analysis, and mining complex types of data. Students also examine applications and trends in data mining.
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