

Probability Theory Review for Machine Learning - peyton
http://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf

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wmclafferty
This is from Andrew Ng's machine learning course, CS229. These notes are
probably from a TA and handed out during a recitation.

You can see the rest of the notes, lectures and other course materials at
[http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a...](http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1)
.

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mahmud
Dunno about this.

People with the mathematical maturity to understand it don't really need the
"review". And the people who need the review, probably don't have the
mathematical background to appreciate this.

Probability theory is not exactly a mathematical field that one can put down
and forget (specially one interested in ML.) You brush against it very often
in other fields, not to mention other _disciplines_.

Aaand to top it off it has no refernces. Not even the requisite "see Feller"
footnote.

~~~
endtime
>People with the mathematical maturity to understand it don't really need the
"review".

I don't really think that holds water. I read through the document and
understood perfectly everything it said, but I couldn't have told you right
before reading it how the variance of a distribution is defined.

Plus, even if everyone's up to date on probability, the doc defines the
notation the course will use. It's unlikely that people with different
backgrounds all share common notation and (to some extent) terminology. 229 is
a class for grad students as well as "advanced undergrads", so much of the
class won't have learned probability in the same place. Hell, I took
probability at Stanford, and if we ever used omega to denote an outcome space,
I don't remember it.

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jimbokun
Andrew Moore's tutorials are also outstanding for people new to Machine
Learning or in need of a refresher.

<http://www.autonlab.org/tutorials/>

Ones useful for reviewing probability theory include:

<http://www.autonlab.org/tutorials/prob.html>
<http://www.autonlab.org/tutorials/pdf.html>
<http://www.autonlab.org/tutorials/gaussian.html>

But they're pretty much all excellent.

