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ISC 1057 Computational Thinking |
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Young Hwan Kim
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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 2310 Data Science with Python |
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Jingze Zhang
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| This course investigates strategies behind popular computational methods used in data science. In addition, many of the algorithms are implemented using the programming language Python. No prior programming experience is required so the course presents the basics of the Python language as well as how to leverage Python’s libraries to solve real-world problems in data science. Prerequisite: MAC 1105 or equivalent. |
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ISC 3313 Introduction to Scientific Computing (with C++) |
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M W F 1:20-2:10 (152 DSL)
TBD
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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. Satisfies FSU Computer Competency requirement. Prerequisites: MAC 2311 (Calculus I) |
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ISC 4220C Continuous Algorithms for Science Applications |
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M W F 9:20-10:10 (152 DSL), T 3:05-5:35 (Lab, 152 DSL)
Sachin Shanbhag
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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. Prerequisite: MAC 2312. |
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ISC 4933 / ISC 5318 High-Performance Computing |
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T R 9:45-11:00 (499 DSL)
Xiaoqiang Wang
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| Introduces high-performance computing, the use of parallel supercomputers, computer clusters, and software and hardware, to speed up computations. Students learn to write faster code that is optimized for modern multi-core processors and clusters, using modern software-development tools and performance analyzers, specialized algorithms, parallelization strategies, and advanced parallel programming constructs. Prerequisite: ISC 5305. |
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ISC 4304C Programming for Science Applications |
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T R 9:45-11:00 (152 DSL), F 3:05-5:35 (Lab, 152 DSL)
Peter Beerli
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| 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. |
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ISC 4943 Practicum in Computational Science |
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T R 3:05-4:20 (499 DSL)
Gordon Erlebacher
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| 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. |
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ISC 5314 Verification and Validation in Computational Science |
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T R 1:20-2:35 (152 DSL)
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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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T R 8:00-9:15 (499 DSL), R 3:05-5:35 (Lab, 152 DSL)
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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 mesh generation, stochastic methods, basic parallel algorithms and programming, numerical optimization, and nonlinear solvers. Prerequisite: ISC 5315. |
Spring 2023 Courses
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ISC 4245C / CAP 5771 Data Mining |
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T R 11:35-12:50 (499 DSL)
Gordon Erlebacher
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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 4302 / ISC 5307 Scientific Visualization |
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T R 8:00-9:15 (152 DSL)
Xiaoqiang Wang
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| 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. Undergrad Prerequisites: COP 3330, ISC 3222, ISC 3313 or ISC 4304, or instructor permission. Graduate Prerequisites: ISC 5305. |
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ISC 4933 / ISC 5935 Data Science Meets Health Science |
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M W F 10:40-11:30 (152 DSL)
Olmo Zavala Romero
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| This course will focus on the applied data science pipeline of data acquisition, data processing and integration, data modeling and analysis, and validation and delivery, commonly used in the Health industry. Topics include data normalization, scientific visualization, multivariate regression, and Artificial Neural Networks (dense, convolutional, recurrent, and adversarial). The examples and projects of this course contain 1D to 4D health data of electrocardiogram sequences, X-ray, Magnetic resonance imaging (MRI), and functional MRI images. |
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ISC 4933 / ISC 5935 Computational Probabilistic Modeling |
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TR 11:35-12:50 (152 DSL)
Nick Dexter
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| In this course, students are introduced to probabilistic programming and modeling for modern data science and machine learning applications. Algorithms for predictive inference are covered from a theoretical and practical viewpoint with an emphasis on implementation in Python. Topics include an introduction to probability and learning theory, graph-based methods, machine learning with neural networks, dimensionality reduction, and algorithms for big data. |
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ISC 4931 Junior Seminar |
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F 12:00-12:50 (422 DSL)
Alan Lemmon
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| Junior Seminar in Computational Science. |
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ISC 5934 Graduate Seminar |
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F 3:05-3:55 (499 DSL)
Sachin Shanbhag
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| A series of lectures given by faculty on the research being conducted. |
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ISC 5939 / GFD???? Advance Grad Seminar in Fire Dynamics |
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T 12:00 - 3:00 (Keen 118)
Kevin Speer
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| TBA |
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Course # TBD Colloquium |
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W 3:30 - 4:30 (499 DSL)
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| Weekly colloquium given by invited speakers to showcase research. |
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GFD 5500L Fire Dynamics Field School |
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(Keen 118)
Kevin Speer
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| TBA |
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GFD 6935 Fire Dynamics Seminar |
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T 12:00 - 3:00 (Keen 118)
Kevin Speer
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| TBA |
