<div>I would suggest following books:</div><div><br></div><div><div><a href="http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=pd_bxgy_b_text_b">Pattern Recognition and Machine Learning </a></div>
<div><a href="http://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=pd_rhf_shvl_3">The Elements of Statistical Learning</a></div><div><a href="http://www.amazon.com/Introduction-Data-Mining-Pang-Ning-Tan/dp/0321321367/ref=pd_sim_b_6">Introduction to Data Mining</a></div>
</div><div><br></div><div><br></div><div>Good Luck.</div><div><br></div><div>--Rana</div><div><br></div><div><br></div><br><br><div class="gmail_quote">On Wed, Jan 5, 2011 at 10:52 PM, Paul Ivanov <span dir="ltr"><<a href="mailto:pivanov314@gmail.com">pivanov314@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><div><div></div><div class="h5">Venkatraman S, on 2011-01-06 10:16, wrote:<br>
> On Thu, Jan 6, 2011 at 9:43 AM, Venkatraman S <<a href="mailto:venkat83@gmail.com">venkat83@gmail.com</a>> wrote:<br>
><br>
> ><br>
> > I have gone through Collective Intelligence and Python text Processing with<br>
> > NLTK, and was wondering whether there is any suggested reading to get into<br>
> > deeper depths in statistical machine learning - something which gives a<br>
> > 'basic' introduction to mixture models , EM etc.<br>
> ><br>
><br>
> Let me expatiate a little more before you point me to the Measuring Measure<br>
> link or Andy Ng classes : I have seen them and have attended the first 3<br>
> lectures of Andy. I also went through courses taught at MIT(via OCW) and<br>
> Jordan's classes(UCB) - but most of these stuff are heavy theoretical - not<br>
> that I am against theory, but i want some hands-on to understand how theory<br>
> is implemented.<br>
> For eg. PythonTextProcessingUsingNLTK does a great job in understanding<br>
> various aspects of text processing by playing with text parallely. Is there<br>
> something similar to understand kernel methods or mixture models?<br>
> To give you one more idea, i asked this question in<br>
</div></div>> stackoverflow<<a href="http://stats.stackexchange.com/questions/5960/how-to-identify-a-bimodal-distribution" target="_blank">http://stats.stackexchange.com/questions/5960/how-to-identify-a-bimodal-distribution</a>>.Working(handson)<br>
<div class="im">> on data while understanding/learning them is a great way to learn :)<br>
<br>
</div>I was the TA for Vision Science 265 - Bruno Olshausen's Neural<br>
Computation course[1] this past semester at UCB:<br>
<br>
This course provides an introduction to the theory of neural<br>
computation. The goal is to familiarize students with the<br>
major theoretical frameworks and models used in neuroscience<br>
and psychology, and to provide hands-on experience in using<br>
these models. Topics include neural network models,<br>
supervised and unsupervised learning rules, associative<br>
memory models, probabilistic/graphical models, sensorimotor<br>
loops, and models of neural coding in the brain.<br>
<br>
It was the first year we allowed the students to do the "lab"<br>
assignments in Python, and I wrote up the templates for most of<br>
them. The assignments involve little toy data sets and boiler<br>
plate code to get you going - most students find them pretty<br>
engaging (I know I did when I took the course 4 years ago).<br>
Videos for all of the lectures are up on Archive.org and linnked<br>
from [1]. Check the syllabus for the topics covered.<br>
<br>
Also, although I have not read it - there's Stephen Marsland's<br>
_Machine Learning: An Algorithmic Perspective_ [2], which comes<br>
with a lot of python code, as well.<br>
<br>
1. <a href="http://redwood.berkeley.edu/wiki/Vs265" target="_blank">http://redwood.berkeley.edu/wiki/Vs265</a><br>
2. <a href="http://www-ist.massey.ac.nz/smarsland/MLbook.html" target="_blank">http://www-ist.massey.ac.nz/smarsland/MLbook.html</a><br>
<br>
best,<br>
<font color="#888888">--<br>
Paul Ivanov<br>
314 address only used for lists, off-list direct email at:<br>
<a href="http://pirsquared.org" target="_blank">http://pirsquared.org</a> | GPG/PGP key id: 0x0F3E28F7<br>
</font><br>-----BEGIN PGP SIGNATURE-----<br>
Version: GnuPG v1.4.10 (GNU/Linux)<br>
<br>
iEYEARECAAYFAk0lZr4ACgkQe牤僳玨 콋掴鑁̅ꅓ�<br>
J9YAoJCTYmBYlK7moG0TGElfDb7SuKbJ<br>
=GAzp<br>
-----END PGP SIGNATURE-----<br>
<br>_______________________________________________<br>
Baypiggies mailing list<br>
<a href="mailto:Baypiggies@python.org">Baypiggies@python.org</a><br>
To change your subscription options or unsubscribe:<br>
<a href="http://mail.python.org/mailman/listinfo/baypiggies" target="_blank">http://mail.python.org/mailman/listinfo/baypiggies</a><br></blockquote></div><br>