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ISC 1057 Computational Thinking |
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T R 12:30-1:45 217 HCB
Dennis Slice
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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. 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. |
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ISC 3313 Introduction to Scientific Computing |
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M W F 1:25-2:15 152 DSL
Alan Lemmon
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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 Java. Prerequisites: MAC 2311, MAC 2312. |
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ISC 4220C Algorithms for Science Applications I |
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M W F 9:05-9:55 152 DSL, T 3:30-6:00 (Lab) 152 DSL
Chen Huang
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| Basic computational algorithms including interpolation, approximation, integration, differentiation, and linear systems solution presented in the context of science problems. The lab component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results. Corequisite: ISC 3222; Prerequisite: MAC 2312. |
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ISC 4304C Programming for Scientific Applications |
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M W F 10:10-11:00 152 DSL, M 2:30-5:00 (Lab) 152 DSL
Peter Beerli
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| Provides knowledge of Python, which serves as a front-end to popular packages and frameworks, along with the compiled language C++. Topics include the practical use of an object-oriented scripting and compiled language for scientific programming applications. There is a laboratory component, concepts learned are illustrated in several science applications. Prerequisites: MAC 2312, COP 3014 or ISC 3313. |
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ISC 4933/ISC 5228 Monte Carlo Methods |
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T R 9:30-10:45 152 DSL
Sachin Shanbhag
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| 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. |
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ISC 4933/ISC 5237 Uncertainty Analysis |
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T R 11:00-12:15 152 DSL
Ming Ye
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| Theoretical foundations and practical applications of uncertainty assessment and risk analysis in earth and environmental sciences, with focus on quantification and reduction of uncertainties impacting geological and environmental processes. Course also deals with scientific and technical uncertainty and risk analysis in support of science-based decision-making by scientists, engineers, and regulatory agencies. |
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ISC 4933/ISC 5314 Verification and Validation |
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T R 2:00-3:15 152 DSL
Tomasz Plewa
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| Students learn basic terminology, are exposed to procedures and practical methods used in software implementation validation and in solution verification, employ exact and manufactured solutions, and explore elements of software quality assurance. Introduces essential data analysis techniques and reviews software development and maintenance tools. Aspects of code variation, including validation hierarchy, validation benchmarks, uncertainty quantification and simulation code predictive capabilities are illustrated. Prerequisite: MAC 2312. |
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ISC 5316 Applied Computational Science II |
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M W F 11:15-12:05 499 DSL, R 3:30-6:00 (Lab) 152 DSL
Bryan Quaife
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| 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. |
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 2018spring-all.jpg | Advertisement | 1052 kB |
 2018spring-all.xlsx | Source | 10 kB |
