APPM 3310 Matrix Methods

Summer 2009

Instructor Dr. Ann Scheels
Office ECOT 247
Email ann.scheels@colorado.edu
Office Hours MW 8am - 9am
T,Th 12:00 - 1pm
Fri 8am - 10:45

Syllabus
Readings, problems and solutions
Semester Project info is here!
Homework: The homework that you turn in will be graded. It is your responsibility to work through as many problems as required for you to master the material.
Exams
The exams will be closed book and no calculators or other electronic devices are permitted.
1. Exam I: Wednesday, February 18, in class. The exam will cover the material in Chapters 1 and 2, including homework and examples from lecture.
2. Exam II: Wednesday, April 8. The exam will cover the material in Ch. 3-Ch. 5.
3. The Final Exam is cumulative. The Final is Saturday, May 2 .
4. Here are some exams from previous semesters. Please note that not all of the first exams correspond to first exam material for our class. In other semesters, different texts may have been used and the material may have been covered at a different pace.

Matlab Examples
1. GaussElim.m, a simple program to do Gaussian Elimination on a regular matrix
2. PGaussElim.m, Gaussian Elimination with permutation for a nonsingular matrix.
3. Face Data is data you can use to try out the solftware below.
4. Principal Components is code you can use to calculate the principal components of 2D data. Try it on the data given above.
5. Gaussian and Confidence are routines that calculate the confidence in a measurement, relative to given data.
6. Eigshow is a Matlab demo of the key idea behind the SVD.
7. This gives more information about the SVD, including applications.
A few years ago, some people (D. Bundy, E. Gibney, J. McColl, M. Mohlenkamp, K. Sandberg, B. Silverstein, P. Staab, and M. Tearle) in Applied Math developed a method to gently teach mathematical writing. Martin Mohlenkamp maintains the Good Problems site that this effort produced. This is an excellent site for guidelines on how to write mathematics well.