Spring 2020 Courses

ISC 1057
Computational Thinking
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. 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.
ISC 3313
Introduction to Scientific Computing
M W F 1:25-2:15 152 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 object-oriented programming language, to facilitate the student’s implementation of algorithms. The computer language will be Fortran. Prerequisites: MAC 2311, MAC 2312.
ISC 4220C
Continuous Algorithms for Science Applications
M W F 9:05-9:55 499 DSL, T 3:30-6:00 (Lab) 152 DSL
Sachin Shanbhag
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.
ISC 4302/5307
Scientific Visualization
M W F 12:20-1:10 152 DSL
Xiaoqiang Wang
This course covers the theory and practice of scientific visualization. Students learn how to use state-of-the-art visualization toolkits, create their own visualization tools, represent both 2-D and 3-D data sets, and evaluate the effectiveness of their visualizations. Prerequisite: ISC 5305.
ISC 4304C
Programming for Science Applications
T R 9:30-10:45 152 DSL, M 2:30-5:00 (Lab) 152 DSL
Peter Beerli
Provides knowledge of a scripting language that serves as a front end to popular packages and frameworks, along with a compiled language such as C++. Topics include the practical use of an object-oriented scripting and compiled language for scientific programming applications. There is a laboratory component for the course; concepts learned are illustrated in several science applications. Prerequisites: MAC 2312, COP 3014 or ISC 3313.
ISC 4933/5227
Survey of Numerical Partial Differential Equations
T R 11:00-12:15 152 DSL
Tomasz Plewa
This course provides an overview of the most common methods used for numerical partial differential equations. These include techniques such as finite differences, finite volumes, finite elements, discontinuous Galerkin, boundary integral methods, and pseudo-spectral methods.
ISC 4933/5935
Genome Sequencing and Analysis
M W F 8:00-8:55 152 DSL
Alan Lemmon
This course will provide students with training in the current algorithms used to process next-generation sequence data. After lectures designed to bring students up to speed on the cutting edge DNA sequencing technologies, students will develop new algorithms for efficient processing of large amounts of genome-scale data.
ISC 4933/5935
Molecular Dynamics: Algorithms and Applications
T R 12:30-1:45 152 DSL
Chen Huang
This course provides a comprehensive introduction to molecular dynamics simulation algorithms and their corresponding applications in molecular sciences. Prerequisite: MAC 2311, MAC 2312, ISC 5305.
ISC 4943
Practicum in Computational Science
T R 12:30-1:45 499 DSL
Anke Meyer-Baese
This practicum allows students to work individually with a faculty member throughout the semester and meet with the course instructor periodically to provide progress reports. Written reports and an oral presentation of work are required. May be repeated to a maximum of six semester hours, with a maximum of only three semester hour credits allowed to be applied to the Computational Science degree.
ISC 5316
Applied Computational Science II
T R 9:30-10:45 422 DSL, R 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.
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