Statistical Learning

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Course Details

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FREE

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Course Provider

Stanford Online online courses
Stanford Lagunita offers a variety of professional education opportunities in conjunction with many of the University’s schools and departments. We also offer an array of free online courses taught by Stanford faculty to lifelong learners worldwide. We foster collaboration with other education organizations by sharing course material, data-driven research, and source code for enhancements to our open-source platform Stanford Lagunita. We continually experiment to improve what we do throu...
Stanford Lagunita offers a variety of professional education opportunities in conjunction with many of the University’s schools and departments. We also offer an array of free online courses taught by Stanford faculty to lifelong learners worldwide. We foster collaboration with other education organizations by sharing course material, data-driven research, and source code for enhancements to our open-source platform Stanford Lagunita. We continually experiment to improve what we do through creative use of technology, and we share what we learn with the rest of the world.

Provider Subject Specialization
Sciences & Technology
418 reviews

Course Description

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 ...

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). The pdf for this book is available for free on the book website.

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