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Classical equations and diagrams in machine learning

TeX 100.00%

machine-learning-cheat-sheet's Introduction

Machine learning cheat sheet

This cheat sheet contains many classical equations and diagrams on machine learning, which will help you quickly recall knowledge and ideas on machine learning.

The cheat sheet will also appeal to someone who is preparing for a job interview related to machine learning.

Download PDF

machine-learning-cheat-sheet.pdf

How to compile

docker pull soulmachine/texlive
docker run -it --rm -v $(pwd):/data -w /data soulmachine/texlive xelatex -synctex=1 --enable-write18 -interaction=nonstopmode machine-learning-cheat-sheet.tex

LaTeX template

This open-source book adopts the Springer latex template.

How to compile on Windows

Install Tex Live 2014, then add its bin path for example D:\texlive\2012\bin\win32 to he PATH environment variable.
Install TeXstudio.
Configure TeXstudio.
Run TeXstudio, click Options-->Configure Texstudio-->Commands, set XeLaTex to xelatex -synctex=1 -interaction=nonstopmode %.tex.

Click Options-->Configure Texstudio-->Build,
set Build & View to Compile & View,
set Default Compiler to XeLaTex,
set PDF Viewer to Internal PDF Viewer(windowed), so that when previewing it will pop up a standalone window, which will be convenient.
Compile. Use Open machine-learning-cheat-sheet.tex with TeXstudio，click the green arrow on the menu bar, then it will start to compile.
In the messages window below we can see the compilation command that TeXstudio is using is xelatex -synctex=1 --enable-write18 -interaction=nonstopmode "machine-learning-cheat-sheet".tex

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machine-learning-cheat-sheet's Issues

Potential mistake in page eq. (4.38) for Bayes rule for linear Gaussian systems

$\Sigma_{x|y}$ should have ^{-1} on the whole outside.

See reference:
http://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall07/reading/gauss.pdf

current latex template for this book is not satisfactory

When begin to write this book, candidate latex templates are MIT Press latex template - mitpress and Springer latex template - SVMult.

SVMult looks pretty close to my expectation, and its documents are more complete and detailed than mipress, so I choose SVMult to get started quickly.

Though SVMult is good, but it's far from perfect. Actually I prefer this book, Machine Learning: A Probabilistic Perspective very much, thus I wanna make this open source book look like it.

The book MLAPP is from MIT Press, but it looks totally different with the mipress template. I guess although the MLAPP book is based on the mitpress template , it has made a lot of customizations.

In future, I will change current template to mitpress and do a lot of customizations.

If anybody knows there exists such a latex template similar to MLAPP, please let me know, I will be appreciated.

Any future plan for fixing "TODO" in the content?

Thanks.

too many mistakes

could you revise it?

too many mistakes

It is awesome! but I found mistakes, so could you guys spend some time revising it? Thanks.

Hello add this cheat sheet to awesome-cheatsheet

Hello, we have indexd this cheat sheet to awesome-cheatsheet

Thank You !!! 🚀 🚀 🚀

Zlfa Pc x T,th Y

use tikz figures instead?

citation

Hi,

Randomly found this place on Github. Looks interesting. But I guess the book is somewhat based on Kevin Murphy's ML book? You might want to mention this in the README.

Disclaimer: I have no personal connection with Murphy.

Why logarithmic loss function no need to calcalute the distance between Y and f(X)?

in chapterIntroduction.tex, the Logarithmic loss function is defined as \ $L(Y,P(Y|X))=-\log{P(Y|X)}$.

I think f(X) = P(Y|X), and why L(Y, f(X)) = -log(f(X)) can be used as a loss function? It can not tell us the loss between the predicate value and real value.

Hope for your reply.

should be
"round up"