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Many people have their class notes for Scientific Computing and Computation Science classes on the web. The three courses shown below have online materials such as the syllabus, assignments, class notes, etc. Although these classes cover or require as prerequisites some of the material listed under numerical analysis, their focus is more on the application of these numerical methods.

Class Cornell, CIS dept
  401, 402, 403
  part of Computational Science and Engineering Grad Option
Topics series of courses which cover pure and applied scientific computing
Notes - Has online lectures and problem sets
  - Uses Matlab
  - All taught by Andrew J. Pershing

Class University of Utah, School of Computing,
  CS 5210 - Advanced Scientific Computation I and II
  Part of Computational and Engineering Program
Audience graduate students
Main topics I. Numerical Analysis and some parallel programming with MPI
  II. PDEs
Teaching I. "Some emphasis will be put on optimality of discussed algorithms."
Techniques II. "learning by doing"
Book I. Scientific Computing, An Introductory Survey, 2nd Ed., by M. Heath, McGraw-Hill, 2002.
  II. Class Notes written by Chris Johnson [9]

Class Stanford, Computer Science Department
  CSD 238 - Parallel Methods in Numerical Analysis
Audience advanced undergraduate/beginning graduate
Main topics parallel architectures, programming models, matrix computations, FFT,
  fast multipole methods, domain decomposition, graph partitioning
Teaching no exams, there are 4 involved programming projects
Books Numerical Linear Algebra for High-Performance Computers,
  by Jack Dongarra, Ian Duff, Danny Sorenson, and Henk van der Vort, SIAM.
  Domain Decomposition: Parallel Multilevel Methods for Elliptic Partial
  Differential Equations, by Barry Smith, Peter Bjorstad, and William Gropp,
  Cambridge University Press.
  also MPI and architecture books

Class Indiana, Computer Science Department
  P573: Scientific Computing I
Audience beginning graduate students in scientific, engineering, or math disciplines
Main topics high-performance computer architectures, software tools and packages,
  characteristics of commonly used numerical methods, graphical presentation
  of results, and performance analysis and improvement
Teaching final exam, small programming projects which add up to 70% of grade
Books most materials needed on web, only reference books given
  Introduction to Matrix Computations by G.W. Stewart
  High Performance Computing by Kevin Dowd
  Mastering Matlab by Hanselman and Littlefield

Online Notes

Problem Sets and some Solutions

Other References

next up previous
Next: Bibliography Up: Using Teaching Techniques to Previous: Future Preparation
Michelle Mills Strout 2002-06-28