Fall 2017 Courses

ISC 1057
Computational Thinking
T R 2:00-3:15 217 HCB
Janet Peterson
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
T R 11:00-12:15 152 DSL
Ming Ye
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. Prerequisites: MAC 2311, MAC 2312.
ISC 3313
Introduction to Scientific Computing
M W F 1:25-2:15 152 DSL
Tomasz Plewa
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 Fortran. Prerequisites: MAC 2311, MAC 2312.
DIG 3725/ISC 5935
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 4221
Algorithms for Science Applications II
M W F 10:10-11:00 152 DSL, W 2:30-5:00 (Lab) 152 DSL
Chen Huang
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 4223
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 4232
Computational Methods for Continuous Problems
T R 9:30-10:45 152 DSL, T 3:30-6:00 (Lab) 152 DSL
Janet Peterson
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 5238
Integral Equation Methods
M W F 1:25-2:15 499 DSL
Bryan Quaife
An alternative approach for solving a partial differential equation (PDE) reformulates it as an integral equation (IE). This aproach is naturally adaptive, allows high order approximations, handles complex geometry and divergence-free constraints. Applications will be drawn from scattering, incompressible Stokes flow, Maxwell’s equations, and interfacial dynamics.
ISC 4933/ISC 5317
Computational Evolutionary Biology
T R 11:00-12:15 422 DSL
Peter Beerli
This course presents computational methods for evolutionary inferences. Presentation includes the underlying models, the algorithms that analyze models, and the creation of software to carry out the analysis.
ISC 4933/ISC 5935
Computational Forensics
T R 2:00-3:15 152 DSL
Dennis Slice
This course will investigate some of the methods and protocols of Computational Forensics with an emphasis on the analysis and interpretation of physical evidence. Topics will include stature, sex, and ancestry estimation from skeletal remains, DNA analysis, and fingerprint, toolmark, and bloodstain analysis. Students will develop their own simple programs in the R programming language to build and verify models and use existing programs to investigate the processing and analysis of physical evidence.
ISC 5305
Scientific Programming
T R 9:30-10:45 499 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
Sachin Shanbhag
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
Data Mining
M W F 12:20-1:10 499 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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