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Statistical Learning

Statistical Learning


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8.3/10 (Our Score)
Product is rated as #194 in category Data Science

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.

Instructor Details

Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics and machine learning. He has published four books and over 180 research articles in these areas. Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for 9 years, where he helped develop the statistical modeling environment popular in the R computing system. He received his B.S. in statistics from Rhodes University in 1976, his M.S. from the University of Cape Town in 1979, and his Ph.D from Stanford in 1984. Professor Hastie is an elected fellow of the Institute of Mathematical Statistics, the American Statistical Association, the International Statistics Institute, the South African Statistical Association and the Royal Statistical Society. He has received a number of awards and honors, including the Myrto Lefkopolous award from Harvard in 1994, the Parzen Prize for Innovation in 2014, and the Distnguished Rhodes University Alumni award in 2015, and was elected to the National Academy of Sciences in 2018.

Specification: Statistical Learning


36 hours









12 reviews for Statistical Learning

3.2 out of 5
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  1. 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.

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  2. 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.

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  3. 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.

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  4. You–cyuan Jhang

    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.

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  5. Prose Simian

    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. Problems with this one:

    it’s basically just a series of lectures presenting the ideas from the book

    without prior familiarity, or lengthy reading, these probably aren’t adequate for groking the concepts

    the ‘assessment’ is less than cursory (a few sudden death MCQs per week)

    the questions and content for the week are sometimes… loose.

    there’s no real opportunity to practice concepts in a concrete way with feedback.

    I’ll certainly watch the lectures. But these have made their way to youtube, and the ‘MOOC parts’ weren’t worth the signup on Stanford OpenEdu. 🙁

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  6. 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, bachelors degree or significant experience in these areas). The course lectures are a bit drab, honestly. The book is fantastic. I wish the exercises were better thought out. Some of the questions are just arbitrarily hard (with no background content in the lecture or book), and some are just too simple.

    Overall, this is a course I’d recommend for anyone who has the time to go back and forth between topics a lot, or for anyone who already has a background in some of the subjects at least at a Bachelor level.

    I was really excited about this course and wanted to like it but had to be honest in this review. But do read the book the writers have done a great job of it.

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  7. 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 (An Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani). In this light, the course becomes an exceptional gem, because the book is really incredibly good. My recommendation is to take the time to read the book cover to cover, trying many of the excellent exercises in it. Then, as a recap or a refresher, go through this online course. The lectures highlight the most important parts of each chapter and are beautifully paced and presented. You will find that they are a perfect complement to the book and many concepts will become clearer and more concretely established in your mind. However, if you try to take this as a stand alone course, you will be disappointed and likely not learn or retain very much.

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  8. 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)

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  9. 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).

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  10. 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.

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  11. 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.

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  12. Cassie

    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.

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