Scientific Computation

* All courses are pending final approval. *

Undergraduate Courses

SSC 318. Introduction to Statistical and Scientific Computing. An introduction to quantitative analysis using fundamental concepts in statistics and scientific computation. Probability, distributions, sampling, interpolation, iteration, recursion and visualization. Three lecture hours and one laboratory hour a week for one semester.

SSC 222. Introduction to Scientific Programming. Introduction to programming using both the C and Fortran (95, 2003) languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability. Prerequisite: Credit or registration for Mathematics 408K or 408C.

SSC 329C. Practical Linear Algebra I. Matrix representations and properties of matrices; linear equations, eigenvalue problems and their physical interpretation; linear least squares and elementary numerical analysis. Emphasis will be placed on physical interpretation, practical numerical algorithms and proofs of fundamental principles. Prerequisite: Credit or registration for Mathematics 408K or 408C.

SSC 329D. Practical Linear Algebra II. Iterative solution to linear equations and eigenvalue problems; properties of symmetric and nonsymmetric matrices, exploitation of parsity and diagonal dominance; introduction to multivariate nonlinear equations; numerical analysis; selected applications and topics in the physical sciences. Prerequisite: Statistics and Computation 329C, or Mathematics 340L or 341.

SSC 335. Scientific/Technical Computing. Comprehensive introduction to computing techniques and methods applicable to many scientific disciplines and technical applications. Covers computer hardware and operating systems, systems software and tools, code development, numerical methods and math libraries, and basic visualization and data analysis tools. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M and prior programming experience.

SSC 339. Applied Computational Science. Concentrated study in a specific area or areas of application. Areas may include computational biology, computational chemistry, computational applied mathematics, computational economics, computational physics, or computational geology. Prerequisite: Mathematics 408D or 408M, and Statistics and Computation 335 and 329D or the equivalent.

SSC 374C. Parallel Coomputing for Scientists and Engineers. Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Prepares students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linux systems.

SSC 374D. Distributed and Grid Computing for Scientists and Engineers. Distributed and grid computing principles and technologies. Covers common modes of grid computing for scientific applications, developing grid enabled aplications, future trends in grid computing. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M, Mathematiecs 340L, and prior programming experience using C or Fortran on Unix/Linus systems.

SSC 374E. Visualization and Data Analysis for Scientists and Engineers. Scientific visualization principles, practices and technologies, including remote and collaborative visualization. Also introduces statistical analysis, data mining and feature detection. Three lecture hours a week for one semester. Prerequisite: Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linus systems.

SSC 375. Special Topics in Scientific Computation. May be repeated for credit when the topics vary. Prerequisite: Upper-division standing; additional prerequisites may vary with the topic and are given in the Course Schedule.

SSC 379R. Undergraduate Research. Individual research project under the supervision of one or more faculty members. The equivalent of three lecture hours a week for one semester. May be repeated for credit. Prerequisite: Upper-division standing, and consent of instructor.

Graduate Courses

SSC 392M. Computational Economics. Same as Economics 392M (Topic 12). Introduction to the development and solution of economic models of growth, macroeconomic fluctuations, environmental economics, financial economics, general equilibrium models, game theory and industrial economics. The course also includes sections on neural nets, genetic algorithms and agent-based methods and stochastic control theory applied to a variety of economic topics. Prerequisite: Graduate standing.

SSC 393C. Numerical Analysis: Linear Algebra. Same as Computational and Applied Mathematics 383C and Mathematics 383E and Computer Sciences 383E. Survey of numerical methods in linear algebra: floating-point computation, solution of linear equations, least squares problems, algebraic eigenvalue problems. Prerequisite: Graduate standing, either consent of instructor or Mathematics 341 or 340L, and either Mathematics 368K or Computer Sciences 367.

SSC 393D. Numerical Analysis: Interpolation, Approximation, Quadrature, and Differential Equations. Same as Computational and Applied Mathematics 383D and Mathematics 383F and Computer Sciences 383E. Survey of numerical methods for interpolation, functional approximation, integration, and solution of differential equations. Prerequisite: Graduate standing, either consent of instructor or Mathematics 427K and 365C; and Computational and Applied Mathematics 383C, Computer Sciences 383C, or Mathematics 383E or Statistics and Computation 393C.

SSC 394C. Parallel Computing fo Scientists and Engineers. Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Prepares students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linux systems.

SSC 394D. Distributed and Grid Computing for Scientists and Engineers. Distributed and grid computing principles and technologies. Covers common modes of grid computing for scientific applications, developing grid enabled applications, future trends in grid computing. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linux systems.

SSC 394E. Visualization and Data Analysis for Scientists and Engineers. Scientific visualization principles, practices, and technologies, including remote and collaborative visualization. Also introduces statistical analysis, data mining and feature detection. Three lecture hours a week for one semester. Prerequisite: Graduate standing, and Mathematics 408D or 408M, Mathematics 340L, and prior programming experience using C or Fortran on Unix/Linux systems.

SSC 395. Advanced Topics in Scientific Computation. Three lecture hours a week for one semester. Topics are announced in the Course Schedule. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; additional prerequisites vary with the topic and are given in the Course Schedule.

SSC 398T. Supervised Teaching in Statistics and Computation. Supervised teaching experience; weekly group meetings, individual consultations, and reports. Offered on the credit/no credit basis only. Prerequisite: Graduate standing and appointment as a teaching assistant.

SSC391D. Data Mining. Focuses on various mathematical and statistical aspects of data mining. Topics covered include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality reduction). The technical tools used in the course draw from linear algebra, multivariate statistics and optimization. Prerequisites: Graduate standing and Mathematics 341 or equivalent.