Data Clustering Chapter link here
Math 350, Spring 2004
The second semester modeling course does not have the
first semester as its prerequisite. The main reason
for this is that we'll be doing multivariate modeling,
which is quite different from the one dimensional version.
In this course, we will primarily be doing Empirical
versus analytic modeling- that is, we'll be doing lots
of data analysis.
- Course Syllabus
- Textbook: Introduction to Empirical Modeling. Below are
PDF and PostScript (PS) versions. The postscript copies are
clearer, but only use if you have a postscript viewer (all
the computers in the Mathlab have postscript viewers). If
you're on a Windows machine, use the PDF versions.
- Contents, Chapter 1, Chapter 2 (PDF)
- Contents, Chapter 1, Chapter 2 (PS)
- Chapter 3 (Learning) in PDF
- Chapter 3 (Learning) in PS
- Ending of Chapter 3 plus Chapter 4 (in PDF)
- Ending of Chapter 3 plus Chapter 4 (in PS)
- New Chapter 4 and Chapter 5 (in PDF)
- End of Chapter 5 through Chapter 7 (Only in PS)
This is a big file (about 4 MB) due to the eigenface images. You'll
probably only want to download this from the Math Lab.
- Neural Nets and RBFs (in compressed PS)
(Download and run "uncompress Ch11-12.ps.Z" to get the PS file).
- Fourier Analysis (in compressed PS)(Download and run "uncompress FourierChapter.ps.Z" to get the PS file).
Listing of the Homework week by week
- Letter E data
- Card Game for Chapter 3 (right click
and "Save Link Target As...")
- The "banditE" program for reinforcement
learning on the n-armed bandit problem
- Driver for the banditE program
(that is, use this script file to run banditE.m
Exam 1 Materials
- Review Questions, Part I
- Review Questions, Part II
- Solutions to (12), Part II Review
Chapter 6 (KL) materials
Chapter 11 (Radial Basis Functions) materials
Chapter 12 (Feed Forward Neural Networks
Final Project Materials: DUE by Tuesday, May 18th- No exceptions
(you can turn them in sooner than that)