# Statistical Learning offered by Stanford University

• Private University
• 8180 acre campus
• Estd. 1885

## Statistical Learning at Stanford University Overview

 Duration 20 hours Total fee Free Mode of learning Online Schedule type Self paced Difficulty level Intermediate Official Website Explore Free Course Course Level UG Certificate

## Statistical Learning at Stanford University Highlights

• Earn a Certificate of completion from Stanford School Of Humanities And Sciences on successful course completion
• Instructors - Trevor Hastie and Robert Tibshirani
• Learn supervised learning, regression and classification methods

## Statistical Learning at Stanford University Course details

Skills you will learn
Who should do this course?
• This course is designed for those who want to learn the essentials of statistics, mainly supervised learning and unsupervised learning.
What are the course deliverables?
• The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013).
• 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. It focuses on 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.

## Statistical Learning at Stanford University Curriculum

Linear and polynomial regression

Logistic regression and linear discriminant analysis

Cross-validation and the bootstrap model selection and regularization methods

Nonlinear models, splines and generalized additive models

Tree-based methods, random forests and boosting

Support vector machines

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