Very concise notes on machine learning and statistics.
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images
README.md
adagrad.html
adagrad.rmd
angles.html Additional notes Jul 27, 2012
angles.rmd
compile.R Additional notes Jul 27, 2012
compile.sh
convexity.html
convexity.rmd
functions.html
functions.rmd
hashing_trick.html
hashing_trick.rmd
knn.html Additional notes Jul 27, 2012
knn.rmd Additional notes Jul 27, 2012
linear_regression.html Additional notes Jul 27, 2012
linear_regression.rmd Additional notes Jul 27, 2012
local_vs_global.html Additional notes Jul 27, 2012
local_vs_global.rmd Additional notes Jul 27, 2012
logistic_regresion.html
logistic_regresion.rmd
mcmc.html
mcmc.rmd
nonparametrics.html
nonparametrics.rmd
norms.html Updated norms Jul 21, 2012
norms.rmd
optimization.html
optimization.rmd
optimization_algorithms.html
optimization_algorithms.rmd Additional notes Jul 27, 2012
outline.html
outline.rmd
regularization.html Revised outline and regularization notes Jul 21, 2012
regularization.rmd Revised outline and regularization notes Jul 21, 2012
rosetta.html
rosetta.rmd
special_matrices.html Additional notes Jul 27, 2012
special_matrices.rmd Additional notes Jul 27, 2012
svd.html Additional notes Jul 27, 2012
svd.rmd

README.md

MLNotes

Notes written for myself to keep ML/stats theory ideas organized. Will hopefully be useful to others.

To best take advantage of these notes, you should download the HTML files and view them in any browswer that supports MathJax. Eventually I'll organize them more clearly and maybe even make a PDF, but for now I'm just keeping ideas in a file named based on the central theme of that file.

Compile Notes

To make sure the notes you're using are up-to-date, it's probably a good idea to compile the notes using the knitr package in R. Once it's installed, you can just run:

bash compile.sh

which will generate the appropriate HTML files.