
Final project reports from 2012 Stanford Machine Learning class - admp
http://cs229.stanford.edu/projects2012.html
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forrestthewoods
Maybe it's just me, but I absolute loathe the format and language style of
"proper" papers. The column view heavy on text and weak on media feels highly
outdated. They come across as a wall of words that almost purposefully seem
difficult to read. It can't possibly be the best way to convey complicated
information. I'll take a slightly less formal, media content rich blog post
every day of the week.

~~~
Bjoern
Coming from a research background I can totally understand your feeling. Once
you have read enough of those papers, you start to kind of skim through them
more easily.

Unfortunately, quality of papers and their readability highly depend on the
skill of the author. There are some papers which are easy and entertaining to
read because of the writing skill and effort put into it. Many though are not
nice at all.

Here is an entertaining but serious one...
<http://www.cl.cam.ac.uk/~rja14/Papers/cocaine.pdf>

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rckrd
I particularly thought the "Analyzing CS 229 Project Topics" was clever.

[http://cs229.stanford.edu/proj2012/ChangSaeta-
AnalyzingCS229...](http://cs229.stanford.edu/proj2012/ChangSaeta-
AnalyzingCS229Projects.pdf)

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__sb__
For anyone interested, a few weeks ago, someone posted a paper on the popular
data mining / machine learning algorithms that gives a brief overview of some
common algorithms [1]. Someone also posted a few presentations on them if you
just want a bullet point summary of the gist of each algorithm [2].

I just finished looking through both. They're both great if you're hoping to
get some traction when it comes to learning ML.

[1] - <https://news.ycombinator.com/item?id=4938162>

[2] - <http://www.cis.hut.fi/Opinnot/T-61.6020/2008/>

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donquix
"Machine learning application on detecting nudity in images" Trying to make it
awkward with the professor.

~~~
raverbashing
I bet the part they most enjoyed was creating the initial training set.

(if it's supervised learning they use, I haven't read the paper)

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stadeschuldt
All reports as a zip archive: <http://ul.to/6wix6k9y>

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sharkweek
I absolutely love looking at other people's ideas -- it's so motivating and
inspirational. The ideas of others so often spark my own desire to get
cracking on a few new projects.

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biot
Based on the nature of the course, I assume these reports are all generated
using Markov chains or similar techniques?

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ifeltsweet
Some of the projects are genuinely interesting and if funded may have bright
future. Note to investors. It's fascinating to see how many ideas there are,
which are still waiting to be explored and conquered.

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waterlesscloud
Thanks for this. I've recently been focused on machine learning myself and
this is a gold mine. The titles alone made me open a Google doc to list
potential project ideas of my own.

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tharshan09
great find! Wish they would go in more detail or contained a bit of source.
However, I can understand why they would not open source etc to prevent
plagiarism, bit of a shame though.

~~~
cosbynator
There was a 5 page limit :) Also, there was nothing to prevent anyone from
releasing source code, I'd imagine that a lot of projects are out there if you
look carefully on GitHub

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sonabinu
Fantastic list

