Statistical Learning at Stanford Overview
Statistical Learning
at Stanford
Duration | 20 hours |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Statistical Learning
Table of content
- Overview
- Highlights
- Course Details
- Curriculum
Statistical Learning at Stanford Highlights
Statistical Learning
at Stanford
- 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 Course details
Statistical Learning
at Stanford
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).
More about this course
- 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 Curriculum
Statistical Learning
at Stanford
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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Statistical Learning
at Stanford