Fall 2018 Courses
 Details
 Last Updated: Wednesday, 20 June 2018 17:20
ISC 1057 Computational Thinking 

T R 02:003:15 217 HCB
Dennis Slice

This introductory course considers the question of how computers have come to imitate many kinds of human intelligence. The answer seems to involve our detecting patterns in nature, but also in being able to detect patterns in the very way we think. This course will look at some popular computational methods that shape our lives, and try to explain the ideas that make them work. This course has been approved to satisfy the Liberal Studies Quantitative/Logical Thinking requirement. 
ISC 3222 Symbolic and Numerical Computations 

M W F 9:059:55 152 DSL
Peter Beerli

Introduces stateoftheart software environments for solving scientific and engineering problems. Topics include solving simple problems in algebra and calculus; 2D and 3D graphics; nonlinear function fitting and root finding; basic procedural programming; methods for finding numerical solutions to DE's with applications to chemistry, biology, physics, and engineering. Prerequisites: MAC 2311, MAC 2312. 
ISC 3313 Introduction to Scientific Computing 

M W F 1:252:15 152 DSL
TBA

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 objectoriented programming language, to facilitate the student’s implementation of algorithms. Prerequisites: MAC 2311. 
DIG 3725/ISC 5326 Introduction to Game and Simulator Design 

T R 11:0012:15 499 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 Discrete Algorithms for Science 

M W F 10:1011:00 152 DSL, F 2:305:00 (Lab) 152 DSL
Sachin Shanbhag

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. 
ISC 4223C Computational Methods for Discrete Problems 

M W F 11:1512:05 152 DSL, M 2:305:00 (Lab) 152 DSL
Anke MeyerBaese

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 9:3010:45 152 DSL, T 3:306:00 (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 4420/ISC 5425 Introduction to Bioinformatics 

T R 11:0012:15 152 DSL, R 8:009:00 (Lab) 152 DSL
Alan Lemmon

Bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. Course is designed for biologists and biochemists seeking to improve quantitative data interpretation skills, and for mathematicians, computer scientists and other quantitative scientists seeking to learn more about computational biology. Lab exercises are designed to reinforce the classroom learning. 
ISC 4933/ISC 5415 Computational Space Physics 

M W F 10:1011:00 499 DSL
Tomasz Plewa

If you are interested in modeling heavenly bodies, motion of stars in a few body system, evaporation of a molecular cloud, or perhaps stellar explosion, chances are you will need at least a few bits of knowledge provided in this series of lectures and computer labs! This course offers introduction to numerical methods in the context of observational and theoretical astrophysics. The course covers interpolation, approximation, minimization and optimization, solution of linear systems of equations, random number generation, function integration, numerical differentiation, numerical integration of ordinary differential equations, stiff systems of ODEs, survey of methods for partial differential equations (Poisson equation, heat diffusion, hydrodynamics). 
ISC 5305 Scientific Programming 

T R 9:3010:45 499 DSL
Xiaoqiang Wang

This course uses the C language to present objectoriented 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 12:301:45 152 DSL, R 3:306:00 (Lab) 152 DSL
Chen Huang

Course provides students with highperformance 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 F 12:201:10 499 DSL
Anke MeyerBaese

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. 