printer friendly pageSTATS 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
Instructors:
Walther, G. (PI)
;
Chen, C. (TA)
;
Jeong, Y. (TA)
;
Sinai, A. (TA)
;
Smith, H. (TA)
;
Wu, S. (TA)
;
Zhang, Q. (TA)
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
Instructors:
Tibshirani, R. (PI)
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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