I'm in!
2013/11/26 Olivier Grisel <olivier.gri...@ensta.org>
>
> 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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--
Peter Prettenhofer
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