MCS 507 Mathematical, Statistical and Scientific Software

Professor Hanson ( hanson A T uic edu , 718 SEO, x3-2142)

Fall 2004


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  • Comments: This is a new course that is a core course in the new Master of Science Degree program in Mathematics and Information Sciences for Industry (MISI), which became official for Fall 2000. This is the second offering of this course.

  • List of Topics (Preliminary)
    Topics                                                   Hours
    --------------------------------------------------------------
    Introduction                                                 9
    Advanced linear algebra software, including MATLAB and NRC  12
    Random number generators and Monte Carlo simulation software 6
    Statistical software (NRC, other packages as time permits)   6
    Partial differential equation solving and finance applics.   6
    Data management software                                     6
    --------------------------------------------------------------
    Total                                                       45
    


    Grading Policy:


    Computer Projects:

    About 5 Projects will be Announced When Topic has been Discussed in Lectures and When Project is Ready:

    1. Individual Computer Project 1: LU Decomposition Solve and Iterative Refinement , Due Friday 24 September 2004 in Class .

    2. Individual MATLAB Project 2: Method of Least Squares for S&P 500 Index , Due Wednesday 20 October 2004 in Class .

    3. Individual MATLAB Project 3: Eigen-Solutions Linear ODEs for Industrial Rate Computations , Due Monday 08 November 2004 in Class.

    4. Individual MATLAB Project 4: Monte Carlo Integration , Due Friday 03 December 2004 in class.

    5. Individual Computer Project 5: Numerical PDE Application ,   Due 03 December 2003 (see description for details). .


    Exams:

    1. Midterm Exam Topics (about 8th week).
      • LU Decomposition.
      • Linear Algebra Iterative Refinement.
      • Singular Value Decomposition.
      • Least Squares Fit Method.
      • Parameter Sensitivity and Error Propagation.
      • General Linear Model Fit and SVD.
      • General Nonlinear Model Fit.
      • Nonlinear Hybrid (Marquardt and Simplex) Methods.


    Class Demonstration Materials:


    Resource Web Links:


    Web Source: http://www.math.uic.edu/~hanson/mcs507/

    Email MCS 507 Class Comments or Questions to hanson A T uic edu