DSST389

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Computational statistics and statistical algorithms for building predictive models from large data sets. Topics include model complexity, hyper-parameter tuning, over- and under-fitting, and the evaluation of predictive performance. Models covered include linear regression, penalized regression, additive models, gradient-boosted trees, and neural networks. Applications are drawn from many areas, with a particular focus on processing unstructured text and image corpora.

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