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ISC 1057 Computational Thinking |
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T R 2:00-3:15 ONLINE
Sachin Shanbhag
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| 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. We 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. |
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ISC 3222 Symbolic and Numerical Computations |
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M W F 1:25-2:15
Alan Lemmon
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| 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. |
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ISC 3313 Introduction to Scientific Computing with C++ |
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M W F 9:05-9:55
TA
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| 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. |
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DIG 3725/ISC 5326 Introduction to Game and Simulator Design |
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T R 11:00-12:15
Gordon Erlebacher
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| 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. |
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ISC 4221C Discrete Algorithms for Science Applications |
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M W F 10:10-11:00
Peter Beerli
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| 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. Prerequisite: MAC 2311. |
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ISC 4223C Computational Methods for Discrete Problems |
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M W F 11:15-12:05 152
Anke Meyer-Baese
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| 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 4304C. |
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ISC 4232C Computational Methods for Continuous Problems |
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T R 9:30-10:45 152
Bryan Quaife
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| 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 4304C. |
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ISC 4245C/CAP 5771 Data Mining |
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M W 1:15-2:30
Anke Meyer-Baese
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| 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. Prerequisites: COP 3330, ISC 3222, ISC 3313 or ISC 4304, or instructor permission. |
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ISC 4420/ISC 5425 Introduction to Bioinformatics |
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M W F 12:20-1:10
Alan Lemmon
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| 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. |
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ISC 5305 Scientific Programming |
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T R 9:30-10:45
Xiaoqiang Wang
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| 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. |
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ISC 5315 Applied Computational Science I |
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T R 12:30-1:45
Chen Huang
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| 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. |
| File | Description | File size |
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 2020fall-all-REMOTE.pdf | Advertisement | 2751 kB |
 DSC-Courses-2020Fall.xlsx | 12 kB |
