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# 1 - 3 of 3 results for: STATS202

## STATS 202:Statistical Learning and Data Science

Overview of supervised learning, with a focus on regression and classification methods. 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: principal components and clustering (k-means and hierarchical). Prerequisites: STATS 117, CS 106A, MATH 51. Recommended: STATS 191 or STATS 203. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Spr | Units: 3

## STATS 202F:Statistical Learning and Data Science [Flipped]

Overview of supervised learning, with a focus on regression and classification methods. 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: principal components and clustering (k-means and hierarchical).Prerequisites: STATS 117, CS 106A, MATH 51. Recommended: STATS 191 or STATS 203. This class is taught in flipped format, where students watch videos at home and work on assignments during class.
Terms: Win | Units: 3

## STATS 202V:Statistical Learning and Data Science Virtual

Overview of supervised learning, with a focus on regression and classification methods. 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: principal components and clustering (k-means and hierarchical). Prerequisites: STATS 117, CS 106A, MATH 51. Recommended: STATS 191 or STATS 203. [202V only] This class is taught in flipped format, where students watch videos at home and attend the lecture remotely.
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