[Baypiggies] Suggested Reading: Machine learning and Python

[Rana Biswas]

I would suggest following books:

Pattern Recognition and Machine Learning
<http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=pd_bxgy_b_text_b>
The Elements of Statistical
Learning<http://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=pd_rhf_shvl_3>
Introduction to Data
Mining<http://www.amazon.com/Introduction-Data-Mining-Pang-Ning-Tan/dp/0321321367/ref=pd_sim_b_6>


Good Luck.

--Rana




On Wed, Jan 5, 2011 at 10:52 PM, Paul Ivanov <pivanov314 at gmail.com> wrote:

> Venkatraman S, on 2011-01-06 10:16,  wrote:
> > On Thu, Jan 6, 2011 at 9:43 AM, Venkatraman S <venkat83 at gmail.com>
> wrote:
> >
> > >
> > > I have gone through Collective Intelligence and Python text Processing
> with
> > > NLTK, and was wondering whether there is any suggested reading to get
> into
> > > deeper depths in statistical machine learning - something which gives a
> > > 'basic' introduction to mixture models , EM etc.
> > >
> >
> > Let me expatiate a little more before you point me to the Measuring
> Measure
> > link or Andy Ng classes : I have seen them and have attended the first 3
> > lectures of Andy. I also went through courses taught at MIT(via OCW) and
> > Jordan's classes(UCB) - but most of these stuff are heavy theoretical -
> not
> > that I am against theory, but i want some hands-on to understand how
> theory
> > is implemented.
> > For eg. PythonTextProcessingUsingNLTK does a great job in understanding
> > various aspects of text processing by playing with text parallely. Is
> there
> > something similar to understand kernel methods or mixture models?
> > To give you one more idea, i asked this question in
> > stackoverflow<
> http://stats.stackexchange.com/questions/5960/how-to-identify-a-bimodal-distribution
> >.Working(handson)
> > on data while understanding/learning them is a great way to learn :)
>
> I was the TA for Vision Science 265 - Bruno Olshausen's Neural
> Computation course[1] this past semester at UCB:
>
>    This course provides an introduction to the theory of neural
>    computation. The goal is to familiarize students with the
>    major theoretical frameworks and models used in neuroscience
>    and psychology, and to provide hands-on experience in using
>    these models. Topics include neural network models,
>    supervised and unsupervised learning rules, associative
>    memory models, probabilistic/graphical models, sensorimotor
>    loops, and models of neural coding in the brain.
>
> It was the first year we allowed the students to do the "lab"
> assignments in Python, and I wrote up the templates for most of
> them. The assignments involve little toy data sets and boiler
> plate code to get you going - most students find them pretty
> engaging (I know I did when I took the course 4 years ago).
> Videos for all of the lectures are up on Archive.org and linnked
> from [1]. Check the syllabus for the topics covered.
>
> Also, although I have not read it - there's Stephen Marsland's
> _Machine Learning: An Algorithmic Perspective_ [2], which comes
> with a lot of python code, as well.
>
> 1. http://redwood.berkeley.edu/wiki/Vs265
> 2. http://www-ist.massey.ac.nz/smarsland/MLbook.html
>
> best,
> --
> Paul Ivanov
> 314 address only used for lists,  off-list direct email at:
> http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7
>
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