Thanks for the share +1
2013/11/26 Sam Bessalah <samkil...@gmail.com>
> Cant wait for you to teach me some R Olivier ;)
> I enrolled ;)
>
> Sam Bessalah
>
> > On Nov 26, 2013, at 7:04 PM, Olivier Grisel <olivier.gri...@ensta.org>
> 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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