I am not an expert mathematician. I worked on these files to improve my grasp of the subject matter. Hopefully they may help reduce the intellectual obstacles for future neophytes. If any errors have slipped in, I would be grateful to receive an email indicating them.
(phillip.potamites@usc.edu)

basic bayes (^with important revisions: 07-12-21) distributions: (binomial,normal,poisson,gamma,chi) decision trees (and chi-square) basic neural nets (i.e. no backpropogation, yet!) maximum (log-linear) likelihood facetious summary: (similarity,information,log-likelihood)

python: probability functions for python: (binomal,normal,poisson,gamma,chi PDFs and CDFs) calculus functions: (integral and derivative) readme.txt all the above zipped gnuplot: IMPORTANT NOTE: I just realized that cutting and pasting this code seems to result in whitespace issues that screws up gngplot. Thus, saving the page seems essential. Use these files as arguments to gnuplot (:$ gnuplot FILENAME) (at the terminal) to produce tex code for "\input{TEXCODENAME}" (in Latex): binomial normal normal and binomial together normal and chi-square poisson and normal gammas (e.g. chi-square) from a sample: (just make sure these are in the same folder/ or edit the gnuplot file so it can find the data): sample plot samp.txt

The calculus functions are due to "Kirby" at
http://mail.python.org/pipermail/edu-sig/2003-March/002757.html .
The gamma and chi distributions also make use of code for the Lanczos approximation from
http://en.wikipedia.org/wiki/Lanczos_approximation.
On-line class notes at
http://www.informatics.susx.ac.uk/courses/nlp/lecture\-notes/corpus\-2.pdf (John A. Carroll)
and
http://www\-nlp.stanford.edu/$\sim$grenager/cs121/handouts/cs121\_lecture06\_4pp.pdf (Prof. Grenager)
were also helpful.
Of course, Wikipedia, PlanetMath, and MathWorld are all incredible learning tools, and the vast majority of the information here came from those sites.