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

- 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).

- 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

- Homework and Descriptions
- M-file for first problem
- M-file for second problem
- Face data for third problem (NOTE: This is a practice data set containing 10 faces that are 64x64 so that the matrix Y is 4096 x 10).
- Movie data for last problem. This has 109 frames, each 120 x 160 pixels (so that Y1 is 19200 x 109). To view an image, you can use "imagesc(reshape(Y1(:,1),120,160))"

- "edm.m": Matlab function to produce the Euclidean Distance Matrix
- Radial Basis Function Lab: Classification
- Script file for the Classification Problem
- Summary of RBFs
- Iris Data (For HW) (IRIS.NET)

- A short script using Matlab to train a Neural Network (quickNNscript.m)
- Diabetes Data: Build a 8-15-15-2 network with tansigs, a training goal of 0.05 and use the algorithm "trainrp"