[Baypiggies] Suggested Reading: Machine learning and Python
xdevice at gmail.com
Thu Jan 6 20:46:21 CET 2011
I would suggest following books:
Pattern Recognition and Machine Learning
The Elements of Statistical
Introduction to Data
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>
> > >
> > > I have gone through Collective Intelligence and Python text Processing
> > > NLTK, and was wondering whether there is any suggested reading to get
> > > 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
> > 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 -
> > that I am against theory, but i want some hands-on to understand how
> > is implemented.
> > For eg. PythonTextProcessingUsingNLTK does a great job in understanding
> > various aspects of text processing by playing with text parallely. Is
> > something similar to understand kernel methods or mixture models?
> > To give you one more idea, i asked this question in
> > stackoverflow<
> > 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 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 . Check the syllabus for the topics covered.
> Also, although I have not read it - there's Stephen Marsland's
> _Machine Learning: An Algorithmic Perspective_ , 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
> Paul Ivanov
> 314 address only used for lists, off-list direct email at:
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