APPM 3310 Matrix Methods
Fall 2010
Learning Assistants: Emily, Stephen, Paul, Bryan
L.A. Sessions
- Monday, 6-7:30 p.m., ECCR 1B55 - Stephen and Bryan
- Tuesday, 6:30-7:50 p.m., ECCR 137 - Emily and Paul
Course Information
- Lecture 001 is on MWF 9-9:50 a.m. in ECCR 155.
- Lecture 002 is on MWF 2-2:50 p.m. in ECCR 151.
- Syllabus (PDF)
- Class Calendar (PDF)
- Homework problems and
solutions : 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.
- Exam Solutions
- Semester Project (PDF) info is here!
- Text: Applied Linear Algebra, by Peter J. Olver and Chehrzad Shakiban (1st Edition, 1st or 2nd printing)
- Corrections to the first printing (PDF)
- Exams
The exams will be closed book and no calculators or other electronic devices
are permitted.
- Exam I: Friday, October 1, in class
- Exam II: Friday, November 5, in class.
- The Final Exam is cumulative. The final for Lecture 001 is Monday, Dec 13th, 7:30 - 10 p.m. and
the final for Lecture 002 is Monday, December 13th, 1:30 p.m. - 4 p.m.
.
- 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.
Resources
- Matlab Examples
- Matlab Basics
- GaussElim.m, a simple program to do
Gaussian Elimination on a regular matrix
- PGaussElim.m, Gaussian Elimination with permutation
for a nonsingular matrix.
- Face Data is data you can use to try out the solftware below.
- Principal Components is code you can use to calculate the principal components of 2D data. Try it on the data given above.
- Gaussian and
Confidence are routines that calculate the confidence in a measurement, relative to given data.
- Eigshow is a Matlab demo of the key idea behind the SVD.
- 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.
Official CU policy information: