
Ask YC: Machine learning course recommendation. - bluishgreen
I know there is a ton results for a google search, which is precisely my problem. SICP is a 4 letter word that everyone would understand here. Are there any such reputed courses for machine learning that jumps to your mind? Any help is much appreciated.
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kurtosis
A great strategy that I've learned a lot from is to use a database like
citeseer or ISI to find the author's of the most cited papers for a topic and
then find their lectures on videolectures.net

Other's already mentioned Jordan, Bishop and Friedman - these are all great

I really liked Thrun, Burgard, and Fox's text Probabilistic Robotics - they
use a lot of ML like algorithms under very tough constraints (limited CPU and
real-time performance)

Shapire (inventor of AdaBoost) has a good course
<http://www.cs.princeton.edu/~schapire/>

Hinton et. al. have a good advanced course:
<http://www.cs.toronto.edu/~hinton/csc2535/>

Moore from CMU has some good slides too: <http://www.cs.cmu.edu/~awm/10701/>

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ible
Chris Bishop's Pattern Recognition and Machine Learning is a good recent book
aimed at 'advanced undergraduates'. It seems to be 'the book' in ML right now.
Norvig and Russel's AIMA is a good general AI resource. The twice annual
machine learning summer schools get videoed and put up on videolectures.net.
Really good introductory lectures from the basics on up to various application
areas. Even better if you can go to one and pick the presenters brains for a
couple weeks, they are intended for both new graduate students and interested
people from industry. After that there are specialized books in the (many)
subfields.

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brent
I second videolectures as an excellent source. Just listen to the ones you are
interested in! One tip I'd offer is to make sure you understand the math of
each lecture before moving on. Skip over the maths enough and you'll find you
haven't truly learned much.

However, in terms of books I would add Elements of Statistical Learning
(Hastie, Tibshirani, and Friedman). It is an excellent text that covers a lot
of ground. The down side of this of course is that it is written at the
graduate level, so be prepared.

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rw
Read Norvig's introductory text, but don't feel you have to know it all - use
it as a way to pick something you really are interested in (genetic algorithms
are always popular) and read academic literature on the topic (citeseer is a
good resource here). Use Wikipedia, Scholarpedia. Get an intuitive sense of
dynamical systems, problem spaces and landscapes, and chaos theory. Get a grip
on combinatorial explosion, and just how much it can suck for optimization
problems. Learn theory of computation, and glance (more than once) at symbolic
logic.

That will set you on your way! Good luck, it is fascinating stuff.

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aneesh
[http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-
Compute...](http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-
Science/6-867Machine-LearningFall2002/CourseHome/index.htm)

Machine Learning at MIT.

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randy
Some similar resources coming from Berkeley are:

[CS 188] Artificial Intelligence -
<http://inst.eecs.berkeley.edu/~cs188/sp08/lectures.html>

[CS 294] Practical Machine Learning -
[http://www.cs.berkeley.edu/~pliang/cs294-spring08/#administr...](http://www.cs.berkeley.edu/~pliang/cs294-spring08/#administrivia)

[CS 281A] Statistical Learning Theory -
<http://www.cs.berkeley.edu/~jordan/courses/281A-fall07/>

[CS 281B] More Statistical Learning Theory -
<http://www.cs.berkeley.edu/~bartlett/courses/281b-sp06/>

There may be more that I don't know of, but I know these courses are fairly
well regarded (I'm planning to take 188 and 281A next semester).

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neilc
Cool, I will likely also be taking 281A.

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herdrick
Peter Norvig recommends these: Hastie
<http://books.google.com/books?id=VRzITwgNV2UC> Bishop
<http://books.google.com/books?id=yHwhAAAACAAJ> Ripley
<http://books.google.com/books?id=2SzT2p8vP1oC>

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manvsmachine
I have Norvig's AI:AMA in PDF form; if you don't have it already and want it,
send me an email.

~~~
pchristensen
could you put your email address here or in the "about" field of your profile?
(the email address field isn't displayed to other users)

or email a copy to peter at pchristensen dot com

Thanks!

~~~
manvsmachine
Check your email.

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richcollins
I thought this was the canonical ML book:

<http://www.cs.cmu.edu/~tom/mlbook.html>

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neilc
It used to be, but 1997 is a while ago; the Bishop book is more popular
nowadays, I believe.

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pskomoroch
I posted a bunch of machine learning video courses here:

[http://www.datawrangling.com/hidden-video-courses-in-math-
sc...](http://www.datawrangling.com/hidden-video-courses-in-math-science-and-
engineering)

videolectures.net has a ton of material as well

Hastie and Bishop are good books to start with, assuming you have a reasonable
mathematics background.

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signa11
i just bought "programming collective intelligence". it's basically machine
learning for engineers rather than mathematicians.

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mlinsey
<http://www.stanford.edu/class/cs229/>

