[Baypiggies] Suggested Reading: Machine learning and Python

[Christopher Lee-Messer]

The nice thing about The Elements of Statistical Learning is that it
is free to download from the publisher. However, it is not a pure
hands-on/workbook style book.  I think there are code samples in R and
test data sets for it.
-chris

On Thu, Jan 6, 2011 at 11:46 AM, Rana Biswas <xdevice at gmail.com> wrote:
> I would suggest following books:
> Pattern Recognition and Machine Learning
> The Elements of Statistical Learning
> Introduction to Data Mining
>
> 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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-- 
Christopher Lee-Messer MD PhD
Instructor in Pediatric Neurology
Postdoctoral Fellow - Deisseroth Lab
Stanford Medical Center
chris (at) lee-messer.net