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Scientific Computing
Fall 2020
Section: 01  

This course will introduce students from various disciplines to computing as applied to scientific problems. The course aims to provide a theoretical background in computational sciences and insight in understanding how popular algorithms and advances in hardware are used to solve problems drawn from chemistry, physics, biology, and bioinformatics. Topics covered will include simulation techniques, analysis of trajectories, clustering, probabilistic modeling, networks, artificial intelligence, neural networks, population dynamics, and high-performance computing.
Credit: 1 Gen Ed Area Dept: NSM MATH
Course Format: LectureGrading Mode: Student Option
Level: UGRD Prerequisites: COMP211
Fulfills a Major Requirement for: (INFO-MN)
Past Enrollment Probability: 90% or above

Last Updated on JUL-17-2024
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