For a recently taken course in Machine Learning, a substantial part involved learning and applying linear classifiers and clustering algorithms on smaller data sets. In order to summarise the most important material, I created a cheat sheet in LaTeX. I figured someone else might appreciate it as well, so why not make it available for more people than myself?
.pdf can be downloaded here.
.tex-file is on Github here; feel free to modify or add information. Please let me know if you find mistakes!
Note that his document was really only created for my own study purposes, and hence might be of limited use for others. Hopefully not, though.
EDIT: Discussion on Hacker News: http://news.ycombinator.com/item?id=2515612
Beautiful, thank you.
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Fantastic and neat! Thanks!
Very helpful indeed. Machine learning could do with being made more accessible with good quality summaries of information like this.
does it come with a magnifier? good grief.
Cool. Good Job!
Good work! I can see that you have improve your skills even more since last year. Keep it up!
Thanks! Finally I got to study Machine Learning; easily one of the more interesting topics I’ve covered.
beautiful indeed!
Thanks for the cheat sheet :) I will probably use your code to make my own very soon.
The people visiting here might like to generate flash cards from LaTeX too. I have an example here: https://github.com/alexbowe/cardgen
It could do with some improvements :) but it helped me memorize design pattern intents like a mofo.
Thanks! And nice idea with the flashcards. It should be easy to port it too, as every equation is its own command (and thus neatly separated from the rest of the cheat sheet).
Let me know how it turns out!
Genio!!
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Nice work, should have thought about that when i had to learn for my exam!
By the way there is a non-linear version of k-Means, called Kernel k-Means. Since the k-Means algorithm only relies on calculating euclidian distances and these distances can be calculated in a Hilbert space, you can easily transform the standard k-Means to a non-linear “kernelized” version. It’s a bummer i don’t find the papers about it right now…
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Excellent!! Thank you so much!!