- For Students
- Original Software
- Matlab bench results for HP Z800 (dual Intel NMH 3.20 GHZ), and other timing results for simple matrix operations in several languages on several machines, including the HP Z800.
- Here are some links to sites with advice on poster design.
- If you would like to ask me to serve on your Masters or Ph.D. graduate committee, please read this.
- Local ACM Chapter, a club for computer science students and professionals, Pictures from the 2000 Computer Science Club's Geek Olympics
- List of research topics that I would like to work on with local students.
- The Hitchhiker's guides to surviving computer science graduate school, by Ronald Azuma,
- Useful pointers for CS PhD Students, collected by Yolanda Gil at ISI.
- Information for graduate students and those considering graduate study in Computer Science, Computer Engineering and Electrical Engineering, maintained by Tim Finin at UMBC.
- A letter from Duane Bailey, Williams College, to research students in computer science.
- The Graduate School Survival Guide, from the Stanford University Medical School.
The code on this page is placed in the public domain with the hope that others will find it a useful starting place for developing their own software. Most is not well-documented nor thoroughly tested. Other code is available on the research project web pages.
Read about a MATLAB implementation of Q-learning and the mountain car problem here. That page also includes a link to the MATLAB code that implements a GUI for controlling the simulation.
Want to try your hand at balancing a pole? Try one of the following. The most recent version is first.
- download Pole.hs, a Haskell implementation that uses Gtk2hs, or the executable for Intel machines,
- download Pole.java. or run the Java applet,
- download pole.tcl , a Tcl/Tk implementation
Here is code for learning to balance a
pole, used for experiments described in Strategy
Learning with Multilayer Connectionist Representations, by
C. Anderson, in the Proceedings of the Fourth International
Workshop on Machine Learning, Irvine, CA, 1987. It includes C code
and a README explaining how to compile it and run it. You may run the
demo executable to try to balance the pole with the
run-demo-net to demonstrate the training of the
neural network to balance the pole. The graphics display requires X
windows. Here is a screenshot:
C Code for Error Backpropagation
train.c is a C program for training multilayer,
feedforward neural networks with error
backpropagation using early stopping and cross-validation. The program
includes the option of training the networks on a CNAPS Server (see the
section above on Parallel Algorithms). The results are written to
stdout in either
short format or long format. If in short format, the results can be
summarized and sorted by summshort.awk, an
awk script. If in long format, use nnlong-to-short.awk to first convert the
file to short format.
This tutorial in postscript describes how to
train.c program and awk scripts. It also describes how
train.c from within Matlab using functions described below.
A Matlab Wrapper for train.c
Since much of the work in any neural network experiment goes into data
manipulation, we have written a suite of Matlab functions for preparing data,
train.c program, and displaying the results.
For starters, here is nnTrain.m, a function that
accepts arguments like the name of the data matrix, writes data files to be
read by the
train.c program, and starts a background process
train.c program. This has evolved to include many
features we find handy, such as running remotely on another machine, including
on our CNAPS Server. As mentioned above, this tutorial in postscript describes how to
nnTrain.m and other Matlab functions
mentioned below. The LaTeX source file is
available as an example for inexperienced LaTeX'ers.
Also, a compressed tar file is
available containing the LaTeX source and figures.
In addition to summarizing the output of
train.c with the awk
scripts, you may plot histograms of test error for each set of
parameter values included in the short format output file using the Matlab
functions nnRuns.m, to load into Matlab a
matrix containing results of all runs, and nnPlotRuns.m to display one histogram for each
set of parameter values. nnRuns.m needs meanNoNaN.m.
Long format output includes information for learning curves, network responses to test data, and the best weight values for each training run. These can be extracted from the output file and displayed within Matlab using nnResults.m. nnResults calls these Matlab functions: nnParseResults.m, nnPlotCurve.m, nnPlotOuts.m, nnPlotOutsScat.m, nnShowWeights.m, nnDrawBoxes.m, fskipwords.m.
Matlab Code for Real-Time Recurrent Learningrtrlinit.m and rtrl.m are two Matlab functions for initializing and training a recurrent neural network using Williams and Zipser's Real-Time Recurrent Learning algorithm. These functions and others that demonstrate their use are contained in rtrl.tar.gz. This tar file also contains this README file.
rfir.m is a Matlab function for training recurrent networks using a generalization of Williams and Zipser's real-time recurrent learning modified for networks with FIR synapses, based on the work of Eric Wan. An example of its use is in xorrfir.m that trains a recurrent network to form the exclusive-or of two input bits.Octave's compatability with Matlab, I have written a short bit of Octave/Matlab code for training feedforward neural networks with a single hidden layer using error backpropagation with early stopping. Here is a gzipped tar file containing several m-files. The README very briefly explains how to run the two-bit exclusive-or example included in the tar file.
Tabbed Panels for Matlab
We have written some code that implements tabbed panels for Matlab. The implementation makes it very easy to add additional panels to an application. It can be downloaded here as pluggablePanels.tar.gz. It includes a README file and a subset of files needed for the example application of an interface for an EEG recording system. Here is a screenshot:
- Pictures from CS picnic, September, 2001.
- Fort Collins Weather Radar Loop
- Fort Collins Webcams, Fort Collins Air Quality Webcam, 9News, Larimer County Courthouse Construction, downtown Fort Collins
- Northern Colorado Astronomical Society
- Lunar eclipse, November 8, 2003, taken with a 1-megapixel digital camera through the eyepiece of an 8" reflector. Taken by David Anderson.
- Astronomy Picture of the Day, from NASA
- Longs Peak Pictures
- David's Movie
- Poems, in the Yet Another Night Before Christmas theme: my poem titled Twas the Year Before Tenure, and another titled Finals from an unknown author.
- Wind Power in Fort Collins.