Thanks a lot ... I'm in too [?]

Cheers,
Eraldo



On Tue, Nov 26, 2013 at 9:56 PM, Jaques Grobler <[email protected]>wrote:

> Thanks for the share +1
>
>
> 2013/11/26 Sam Bessalah <[email protected]>
>
>> Cant wait for you to teach me some R Olivier ;)
>> I enrolled ;)
>>
>> Sam Bessalah
>>
>> > On Nov 26, 2013, at 7:04 PM, Olivier Grisel <[email protected]>
>> wrote:
>> >
>> >
>> https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about
>> >
>> > """
>> > This is an introductory-level course in supervised learning, with a
>> > focus on regression and classification methods. The syllabus includes:
>> > linear and polynomial regression, logistic regression and linear
>> > discriminant analysis; cross-validation and the bootstrap, model
>> > selection and regularization methods (ridge and lasso); nonlinear
>> > models, splines and generalized additive models; tree-based methods,
>> > random forests and boosting; support-vector machines. Some
>> > unsupervised learning methods are discussed: principal components and
>> > clustering (k-means and hierarchical).
>> >
>> > This is not a math-heavy class, so we try and describe the methods
>> > without heavy reliance on formulas and complex mathematics. We focus
>> > on what we consider to be the important elements of modern data
>> > analysis. Computing is done in R. There are lectures devoted to R,
>> > giving tutorials from the ground up, and progressing with more
>> > detailed sessions that implement the techniques in each chapter.
>> >
>> > The lectures cover all the material in An Introduction to Statistical
>> > Learning, with Applications in R by James, Witten, Hastie and
>> > Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this
>> > book will be available for free, with the consent of the publisher, on
>> > the book website.
>> > """
>> >
>> > I have just registered :)
>> >
>> > --
>> > Olivier
>> > http://twitter.com/ogrisel - http://github.com/ogrisel
>> >
>> >
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