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Trevor Hastie and Robert Tibshirani are teaching online course
(through Stanford's OpenEdX) on statistical learning using R.
ABOUT THIS COURSE
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)
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