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Stanford University

Statistical Learning with R

Stanford University via edX


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; neural networks and deep learning; survival models; multiple testing. 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 science. 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. We also offer a separate version of the course called Statistical Learning with Python – the chapter lectures are the same, but the lab lectures and computing are done using Python.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R (second addition) by James, Witten, Hastie and Tibshirani (Springer, 2021). The pdf for this book is available for free on the book website.

Taught by

Rob Tibshirani and Trevor Hastie


4.1 rating, based on 28 Class Central reviews

4.4 rating at edX based on 39 ratings

Start your review of Statistical Learning with R

  • HChan
    First, a disclaimer: the online exercises of this course are extremely thin, so your score in this class is neither necessary or sufficient to gain mastery of the material. It helps if you think of this course as supplementary material for the book…
  • Good book, terrible MOOC. First of all: huge kudos to Hastie and Tibshirani for their contributions to the field, and making their seminal books freely available. None of this is directed personally at them - it's difficult to design a good MOOC. Pr…
  • Rajesh
    Took this course (or at least parts of it) on the Stanford Online platform. Unfortunately, this course is geared towards people who already have some knowledge of the mathematics, statistics and programming concepts in a classroom (typically, bachel…
  • Samuel Webber
    Poorly done video lectures in which the instructors simply read from the slides. Also the quiz questions weren't very helpful for testing your knowledge of the material or helping with retention. I got the impression the quiz questions were thrown together last minute simply because the edx platform required quiz questions be inserted at some point.
  • Profile image for Procellaria
    The course is a good view of the supervised learning methods. Most of the lessons are clear and self-consistent, in some cases, a pre-existing knowledge of statistical concepts is necessary for a full understanding. The teachers pay special attention to introduce to the proper use of the techinques. The R sessions are useful and clear. Nevertheless, the course can be improved in several points (in my opinion Ch9 and Ch10 are hasty, the tree-based methods are introduced properly but the explanation of random rorests and boosting are not completely clear).
  • Pros: This course will give you a quick introduction to common machine learning algorithms and basic principles of data science implementation. The course material and video is very concise and fun to watch. Recommended for beginner with basic statistical analysis background.

    Cons: Only limited programming assignment are provided. I highly recommended you to follow homework/examples in the book.
  • Eunhee Hwang
    I deeply appreciate the university and the professors who made this course "free." I am a practicing statistician with a Ph.D. degree. I wanted to learn "new" statistics; Following the Element of Statistical Learning book by myself was tough; This course became a nice bridge in terms of brushing up my R and learning the new concepts. Excellent course from excellent teachers!
  • Ricardo Vladimiro
    Fantastic course, probably the best I took so far. If you are interested in statistical learning and/or R, this is an absolute must have.
  • Santosh Goteti
    A very nice course. The concepts are clearly explained. However, the assignments are very easy, and do not give you enough practice to master the concepts.
  • Anonymous
    It was awesome and great class. Here we'll learn about Statistic as well as R programming. Amazing advise everyone to enroll into this program (who are interested in learning Statistic)
  • Profile image for David Chen
    David Chen
    It was so refreshing to actually take a course taught by Drs. Rob and Trevor, whose research papers I had been reading before taking the course. The course was pretty much a companion to their textbook "Introduction to Statistical Learning", but "summarizes" the dense, theoretical textbook in a rather applied manner. I absolutely loved the instructors' lectures and R programming demos. The course exercises were fairly straightforward and help reinforce the concepts covered. I would love to see the instructors teaching new MOOCs!
  • Anonymous
    This was a wonderful course. The professors gave the impression that the material was interesting, learnable and even fun. One has the feeling of actually being in the classroom. I could not help thinking about "car talk" on public radio. A great introduction to valuable, timely information. Yes you have to work at it but you will be rewarded.
  • It is not a easy class but it worth to spend your time on it. After taking the class, I have a great improvement in R programming and machine learning. And it has a free textbook, which is also a great book.
  • Aryan completed this course, spending 5-6 hours a week on it and found the course difficulty to be easy to moderate.

    The course is a good companion to the book, not the other way round.
  • Ava
    Very good textbook, however the course left much to be desired. The lecture videos are quite dry and the review quizes are not well designed.
  • Jelle De Jong
  • Chaitra
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    Ilya Rudyak
  • Vlad Podgurschi
  • Andrew Pribram

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