|
ISC 1057 Computational Thinking |
|---|
|
T R 2:00-3:15 217 HCB
John Burkardt/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 9:30-10:45 152 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 |
|---|
|
T R 2:00-3:15 152 DSL
Peter Beerli
|
| 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 Java. Prerequisites: MAC 2311, MAC 2312. |
|
DIG 3725/ISC 5935 Introduction to Game and Simulator Design |
|---|
|
T R 9:30-10:45 499 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. |
|
ISC 4221 Algorithms for Science Applications II |
|---|
|
M W F 10:10-11:00 422 DSL, M 4:00-6:30 (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 10:10-11:00 422 DSL, W 4:30-7:00 (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 12:30-1:45 499 DSL, R 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 4933/5224 Introduction to Bioinformatics |
|---|
|
M W F 11:15-12:05 152 DSL
Alan Lemmon
|
| Bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. 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 reinforce the classroom learning. |
|
ISC 4933/CAP 5771 Data Mining |
|---|
|
M W 8:45-9:50 499 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. |
|
ISC 4933/5935 Geometric Morphometrics |
|---|
|
M W F 12:20-1:10 048 BEL
Dennis Slice
|
| Morphometrics, or shape analysis, contributes to many diverse research areas. The shape of an organ or organism may affect its function and can reflect developmental state, ecological adaptation, and/or evolutionary history. We’ll examine the methods and tools of shape analysis with emphasis on the latest geometric methods, especially coordinate-based methods for analysis of anatomical landmark locations. |
|
ISC 5305 Introduction to Scientific Programming |
|---|
|
T R 11:00-12:15 499 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, M 1:30-4:00 (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. |
|
ISC 5316 Applied Computational Science II |
|---|
|
T R 11:00-12:15 152 DSL, T 3:30-6:00 (Lab) 152 DSL
Tomasz Plewa
|
| Provides students with high performance computational tools to investigate problems in science and engineering with an emphasis on combining them to accomplish more complex tasks. Topics include numerical methods for partial differential equations, optimization, statistics, and Markov chain Monte Carlo methods. Prerequisite: ISC 5315. |
|
MAP 5395 Finite Element Methods |
|---|
|
T R 9:30-10:45 422 DSL
Max Gunzburger
|
| Finite element methods are often the method of choice for determining approximate solutions of partial differential equations that describe a wide spectrum of physical, ecological, biological, social, and economic settings. The course introduces the algorithmic aspects of finite element methods and of their implementation. The mathematics of finite element methods is also considered, especially as it relates to the estimation of errors in the approximate solution. |
