M331 Introduction to Mathematical Modeling
Fall 2002

Important Course Dates:

Computer Lab Days (all students go to Weber 205 now)
Text: This course will be based on class notes. FINAL UPDATE (125 pages) Meeting times: MWF 11, EE-205.
Instructor: Professor Kirby, Weber 115, kirby@math.colostate.edu
Office Hours: Monday 1:20-2:10; Wednesday 10:00-11:00, Thursday 10:30-11:30.
Teaching Assistant: Amanda Fox, Weber 10, fox@math.colostate.edu
Office Hours: Monday, Friday 12:00-12:50. Tuesday 3:10-4:30
Prerequisite: M160;   Co-Requisite: M161
WEB: http://www.math.colostate.edu/~kirby/teaching/m331/Fall2002/M331.htm

Topics Outline
  •      The Modeling Process
  •      Modeling with Discrete Dynamical Systems
  •      Modeling Using Proportionality
  •      Model Fitting
  •      Simulation Modeling
  •      Continuous Optimization Models
  •      Linear Programming
  •      Modeling with Differential Equations
  •      Probabilistic Modeling

  •      Dimensional Analysis and Similitude


Homework Assignments (Homework is due every Wednesday -- no homework will be accepted after class ends.)


Data for Homework Problems

Exam Results




These projects will be the focus of the last two weeks of the semester.  The entire class will meet in the Weber 205 computer lab  during the last two weeks save for the last day of classes which will be held in our regular classroom.  There will be several options for the class project but all will be MATLAB based.  The goal of the class project is to go into further depth into a topic than is possible in a single homework assignment.  The projects will be done in groups of 1-4 with 3 or 4 students being the optimal size.  Consider organizing your groups now.  The project topics will include

  1. Football Passing Ratings:  The rating system the NFL uses is made known to the fans.  In this project you will see how M331 will help you discover this rating system.
  2. Stock Market Predictions: This project will involve the application of radial basis functions to this important problem.
  3. Preditor Prey Simulations: Investigation of larger systems of difference equations.
  4. Simulation Modeling