Statistical Learning

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9/10 stars
based on  66 reviews
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Start Date TBA

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Course Details

Cost

FREE

Upcoming Schedule

  • TBA

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
393 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.

Reviews 9/10 stars
66 Reviews for Statistical Learning

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Michael Devereux profile image
Michael Devereux profile image
8/10 starsCompleted
  • 5 reviews
  • 4 completed
3 years, 7 months ago
This was my second course on machine learning after having taken Prof Ng's Machine Learning - and oh boy, the teaching content and techniques could not have been more different. But this is not a bad thing at all - rather this course takes a much more rigorous theoretical approach towards machine learning. You get to learn a lot more about the motivation behind doing one sort of technique vs. another - and indeed they look much further into the algorithms than on Prof Ng's course. One thing to note is that all lessons (and homeworks) will require R - as opposed to Prof's Ng's course requiring matlab/octave. I believe R is one of the de facto standards for open course statistical analysis and so highly recommend that you take this opportunity to learn R in the course (or maybe take a course focusing on R alongside) rather than see this as an obstacle. Homeworks are a key part of learning for the course - you get quick questions ... This was my second course on machine learning after having taken Prof Ng's Machine Learning - and oh boy, the teaching content and techniques could not have been more different. But this is not a bad thing at all - rather this course takes a much more rigorous theoretical approach towards machine learning. You get to learn a lot more about the motivation behind doing one sort of technique vs. another - and indeed they look much further into the algorithms than on Prof Ng's course. One thing to note is that all lessons (and homeworks) will require R - as opposed to Prof's Ng's course requiring matlab/octave. I believe R is one of the de facto standards for open course statistical analysis and so highly recommend that you take this opportunity to learn R in the course (or maybe take a course focusing on R alongside) rather than see this as an obstacle. Homeworks are a key part of learning for the course - you get quick questions for every video + section quiz for each week's content. Do not underestimate the difficulty of the homeworks, particularly as not all of the questions are well phrased (and this was hotly debated in the forums when I did this course earlier this year). Luckily you have until the end of the course to do all of this in! Despite having said all that about the homeworks I still highly rate this course - knowing the theoretical underpinnings of the most common machine learning techniques taught by world class professors in their field was extremely enlightening for me and their (free) pdf "An Introduction to Statistical Learning" is highly accessible (I have no science/machine learning background and found most of it intuitive enough) and has lots of R examples to follow. And this is all free!
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student

10/10 starsCompleted
3 months, 1 week ago
Although having taken courses in machine learning before, I still find this course useful as it explained ideas in a statistical way. Will look into ESL in the future! Thank you Trevor and Rob!
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Avinash Sneha ravi profile image
Avinash Sneha ravi profile image

Avinash Sneha ravi

7/10 starsTaking Now
1 year, 3 months ago
The videos are very informative and I like that the focus is less on the syntax aspects fr R. But in the entire course I did not find any exercises or assignments...are the quizzes themselves the exercises?
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Bishal Lakha profile image
Bishal Lakha profile image
10/10 starsCompleted
  • 0 reviews
  • 0 completed
1 year, 5 months ago
This is very engaging and informative course. I specially like the way instructors clarify the ideas . Their occasional jokes make this course more enjoyable. Besides that, there are some insightful interviews and watching them was quite inspiring.
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David Trent profile image
David Trent profile image

David Trent

10/10 starsTaking Now
1 year, 5 months ago
Background: I've taken undergrad and grad courses previously in Statistics as part of my engineering degrees. Those were full of derivations, proofs, etc. I also have skimmed various articles, academic papers, etc trying to refresh and expand my predictive learning understanding. My goal was to find a comprehensive course that gave breadth, depth, and a sense of order to what I'd sampled. I took this course after surveying the options and am thrilled! I'm ending chapter six of ten and am getting a pragmatic and applied course that provides context without undo dives into theory - enough theory to understand and appreciate but not to make you a full time grad student. Bravo!
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Khalil Elkhalil profile image
Khalil Elkhalil profile image

Khalil Elkhalil

10/10 starsCompleted
1 year, 5 months ago
Excellent course with amazing instructors. I really like Rob and Trevor and their deep understanding of the subject. As an introduction to statistical learning I can not think of a better course where you could explore things in a comprehensive way.
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Toki Babai profile image
Toki Babai profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
1 year, 6 months ago
In a nut shell, It is gold.I highly recommend to everyone who needs a good understanding well as applied knowledge of Statistics.
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Digant Desai profile image
Digant Desai profile image

Digant Desai

10/10 starsCompleted
1 year, 7 months ago
One of the best MOOC I have ever attended. The content is well balanced for beginner's entry. The professors are the experts. Well structured, follows a free book. Can not ask for more.
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Batyr NG profile image
Batyr NG profile image

Batyr NG

10/10 starsTaking Now
1 year, 8 months ago
The best introductory Statistical Learning course for Machine learning beginners! Thank you very much!
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Pamela Johnston profile image
Pamela Johnston profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
1 year, 11 months ago
I bumped into a very large Whale Shark while diving in Indonesia, had to elevate my leg for 24 hours before I could resume diving, so decided to dig into this course, which I'd downloaded but never gotten around to actually taking. Once started, I was hooked. People on the boat, who saw me reading and listening to the course videos, would ask what I was doing, and I’d say, “I am studying to become a modern data scientist.” I don’t think they believed me, but I truly was. The course is class outstanding! Given I have not used my engineering math skills since 1986, when I left engineering to get an MBA, and haven't programmed since the early 80's (and in Fortran WATFIV!), I probably had to work a lot harder than most to complete this course. It was worth the time and the effort. Rob and Trevor are absolutely superb and the knowledge I gained from this course was interesting and invaluable to me, a business leader (and now a modern da... I bumped into a very large Whale Shark while diving in Indonesia, had to elevate my leg for 24 hours before I could resume diving, so decided to dig into this course, which I'd downloaded but never gotten around to actually taking. Once started, I was hooked. People on the boat, who saw me reading and listening to the course videos, would ask what I was doing, and I’d say, “I am studying to become a modern data scientist.” I don’t think they believed me, but I truly was. The course is class outstanding! Given I have not used my engineering math skills since 1986, when I left engineering to get an MBA, and haven't programmed since the early 80's (and in Fortran WATFIV!), I probably had to work a lot harder than most to complete this course. It was worth the time and the effort. Rob and Trevor are absolutely superb and the knowledge I gained from this course was interesting and invaluable to me, a business leader (and now a modern data scientist, too).
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Irina Max profile image
Irina Max profile image

Irina Max

10/10 starsCompleted
1 year, 11 months ago
The one of the hardest and and very powerful course of Data Science I ever had. I had a lot of courses before and after, and this one is definitely best so far. I was really involved, professors Trevor Hastie and Rob Tibshirani made it easy to do! It was more interesting and intense when it was due time! I proud of my achievements, and I still coming back sometime to clear some points. Thank you so much to help me update my statistical skills and polish R!
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Kurt Godden profile image
Kurt Godden profile image

Kurt Godden

10/10 starsCompleted
2 years, 1 month ago
Like a few others, I have also taken Ng's class before this, and also some data science classes at Coursera. But Hastie and Tibshirani's class is my favorite so far. The only disappointment I felt was that, inexplicably, they do not cover neural nets, so I'm taking Hinton's NN course at Coursera right now. Not only to H&T provide an excellent intro to modern machine learning, they also teach you a lot about effective methodologies to use in building, validating and testing models. The accompanying text is also excellent.
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Igor Kleiner profile image
Igor Kleiner profile image
10/10 starsCompleted
  • 4 reviews
  • 3 completed
2 years, 2 months ago
Great course for beginner and intermediate in data science. The book of course is a better then a course. So probably better option is to read a book of authors. This course mainly practical on R and a bit theoretical. But it is not overloaded with theoretical detail's.
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Kia Vosoughi profile image
Kia Vosoughi profile image

Kia Vosoughi

10/10 starsCompleted
2 years, 5 months ago
A deep look into statistical modeling which provide a vivid prospective of linear and non-linear models. It also introduce "R" with a simple language.
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 profile image

4/10 starsCompleted
  • 1 review
  • 1 completed
2 years, 5 months ago
I have to say that I was expecting better. It was not a terrible class, but I wouldn't say it was a good one. I hear some reviewers saying this was a very theoretical class - I couldn't disagree more. There are no derivations, and you don't learn to implement any method - you just use R packages to do it. You do learn a little bit (very tiny bit) about statistics, but not much else. I know what you may be thinking: "there is no need in implementing what is already available". That's true, but when you are taking a class on something, you expect to learn how the methods work, not just use some black box. In practical implementations you will probably just use some library, but if I was to learn that I would just read the docs. Besides, when I took this, there was a limited number of attempts on the quizzes - and it's a good thing, because otherwise you can just guess until you get it right. However, some of the questions were... I have to say that I was expecting better. It was not a terrible class, but I wouldn't say it was a good one. I hear some reviewers saying this was a very theoretical class - I couldn't disagree more. There are no derivations, and you don't learn to implement any method - you just use R packages to do it. You do learn a little bit (very tiny bit) about statistics, but not much else. I know what you may be thinking: "there is no need in implementing what is already available". That's true, but when you are taking a class on something, you expect to learn how the methods work, not just use some black box. In practical implementations you will probably just use some library, but if I was to learn that I would just read the docs. Besides, when I took this, there was a limited number of attempts on the quizzes - and it's a good thing, because otherwise you can just guess until you get it right. However, some of the questions were pretty random: they were about topics that hadn't been taught or about things you could only guess. TL;DR: you may learn some interesting stuff if you don't have much of a background on the area. Otherwise, it's a waste of time.
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Richa Saini profile image
Richa Saini profile image

Richa Saini

10/10 starsCompleted
2 years, 7 months ago
The best course with respect to understanding the algorithms and there implementations in machine learning.
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Steven Wink profile image
Steven Wink profile image

Steven Wink

7/10 starsCompleted
2 years, 8 months ago
I enjoyed the course alot, even though the math was easy/largel absent this did not affect the understanding of the algorithms due to the great down to earch explanation by the instructors. Althoug I have studied statistics and statistical learning and applied it professionaly in several projects, this course gave me a great overview of available methods and which considerations are key to selecting the appropriate method. Thank you for an enjoyable course
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Hamideh Iraj profile image
Hamideh Iraj profile image
8/10 starsCompleted
  • 70 reviews
  • 60 completed
2 years, 9 months ago
This was a nice course offered by lovely professors Dr. Hastie and Dr Tibshirani. I liked the lectures. Concepts were explained simply and intuitively. The use of the two professors helped make the course a little bit more engaging. The second professor (The one who was not lecturing) contributed by raising questions and asking for explanations. However, I did not like quizzes. They were far from the course and usually tricky which lead to disappointment and frustration. (Generally I prefer project-based courses) I also enjoyed their effort for making the lectures meaningful by the telling the stories of how the technique was born, by whom and which society (statisticians or computer scientists). This was very valuable and made a lot of sense for me. Just remember that this is not an introductory course. Take it when you have a basic understanding of statistics and machine learning.
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Damian Granados profile image
Damian Granados profile image

Damian Granados

10/10 starsTaking Now
2 years, 9 months ago
Para mí ha sido muy importante tener la posibilidad de acceder a este curso. Lamentablemente, tengo problemas de salud que solucionar, y ello ha hecho que no pueda establecerme en el curso. Agradezco la oportunidad que me ofrecieron de estar por aquí. Muchas gracias. (Prefiero expresarme en mi lengua materna, debido a que mi nivel de inglés no es aún el adecuado). Gracias de nuevo a todos y un fuerte abrazo.
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Tushar Tilwankar profile image
Tushar Tilwankar profile image

Tushar Tilwankar

9/10 starsTaking Now
2 years, 9 months ago
This is my second course from Stanford I must say quality of instructors is very good. Thanks for giving so much valuable information for free.
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4/10 starsDropped
  • 2 reviews
  • 1 completed
2 years, 10 months ago
The quiz questions in this course are so strangely worded and frustrating, often focused more on tricky semantics than anything else. I'm a native English speaker and still struggled with the wording of the questions. I made it through the first few chapters before deciding to drop the course, but getting the right answer to most of the quiz questions felt more like a matter of luck than anything else. I didn't feel like answering them correctly had much to do with how well I understood the material. I kept going back and forth wondering if I should stick with it, since I am interested in the subject matter, but the quizzes were so frustrating that it didn't seem like a good use of my time- even for a free certificate of completion. This course's discussion forums are full of students asking various forms of "huh?" in order to try to understand what the questions are asking, or even to understand the answers which sometimes seem t... The quiz questions in this course are so strangely worded and frustrating, often focused more on tricky semantics than anything else. I'm a native English speaker and still struggled with the wording of the questions. I made it through the first few chapters before deciding to drop the course, but getting the right answer to most of the quiz questions felt more like a matter of luck than anything else. I didn't feel like answering them correctly had much to do with how well I understood the material. I kept going back and forth wondering if I should stick with it, since I am interested in the subject matter, but the quizzes were so frustrating that it didn't seem like a good use of my time- even for a free certificate of completion. This course's discussion forums are full of students asking various forms of "huh?" in order to try to understand what the questions are asking, or even to understand the answers which sometimes seem to contradict what's in the textbook. The accompanying ISLR textbook (also free) is still worth studying. It contains lots of exercises and datasets to work with. Aside from the quiz questions, know that the prerequisites for this course are a bit understated in terms of the amount of statistical fluency you're assumed to have.
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student

9/10 starsCompleted
3 years, 3 months ago
Was excellent and infinitely better that the type of education I get at my undergraduate school in India.
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Zbyněk Zajíc profile image
Zbyněk Zajíc profile image
10/10 starsCompleted
  • 18 reviews
  • 18 completed
3 years, 3 months ago
Very good overview of the problems in Statistical Learning, only the presenters could be less boring. Nevertheless the course is full of information, fully recomanded for the students.
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Student

10/10 starsCompleted
3 years, 3 months ago
World class profs. Free and excellent resources: text books, software etc. Comprehensive, engaging content delivered by experts in their field in an entertaining and compelling fashion. All one needs to do is commit the time and put in the effort then strap yourself in for an excellent learning experience.
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Student

6/10 starsDropped
3 years, 8 months ago
I dropped the course because:. * The course content is very similar to the book "An Introduction to Statistical Learning". There was nothing new nor augmenting in the course. * The two professors do not seem to be active in the discusions, leaving the students interacting among themselves. Thus, challenging questions remain unanswered (see "Correlation inherent to polynomial regression?") * I can compare this MOOC to two other MOOCs I have taken on similar topics related to statistics and R, on the French platform FUN (www.france-universite-numerique-mooc.fr). In both courses, the professors were very present, providing answers in many on the questions asked. * For these 3 reasons, I believe that the value added of the course "Statistical Learning" is limited.
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10/10 starsCompleted
3 years, 8 months ago
Thank you so much for offering this course! I found it very helpful that the course is based on "An Introduction to Statistical Learning". The course and book reinforce each other. The material is very clear, and I really appreciate the attention to detail that went into it, for instance the many helpful illustrations. It was great to be taught by Prof. Hastie and Prof. Tibshirani themselves, as well as Dr. Witten. I really enjoyed the interviews as well.
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Student

10/10 starsCompleted
3 years, 8 months ago
Course content is top notch. This course should be followed alongside the book. Though this course does not make you a machine learning expert overnight, it certainly enables you to appreciate machine learning/Data science better. You'll be able to make sense of any ML discussion and have the confidence to contribute meaningfully to an ML project. I strongly recommend this course and the book for those who wish to get introduced solidly to ML. I'm a working professional not in the area of ML and I have little mathematical background (undergrad level math).
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Student

10/10 starsCompleted
3 years, 8 months ago
Based on the excellent Intro to Stat Learning in R book by the same team. Lectures complement the book with nicely produced slides and an engaging, easy-going presentation with a give and take between the instructors who are, it seems, colleagues and pals. The content has a nice balance in the content between stat/math principles and applications, between depth and breadth. One weak point, as others have observed, is the quizzes, but there are many other exercises in the book to challenge yourself with. Not easy material, and the "distinction" level is hard to achieve, but you will learn a lot.
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Student

10/10 starsCompleted
3 years, 8 months ago
I completed and passed but no where near with distinction. I thought the lectures were well organized and I liked the back and forth of the two professors and the varying teaching style. It took me more effort than advertised to pass the course and it would have taken a ton more to have gotten with distinction. The book is excellent and they point to a link with some unofficial exercise answers. The quizzes were tough. I think there were a couple of questions that seemed to be tricky but most that I got wrong were because I didn't know the answer or was careless. I highly recommend the class. I didn't do as well as I wanted but I learned a lot.
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8/10 starsDropped
3 years, 7 months ago
I dropped the course after completing Ch. 5, because I was overstretched with other responsibilities. Both, Hastie and Tibshirani were wonderful. The course helped me to learn R, at least, I am not a novice. I will continue to develop my knowledge of R using the book provided with the course. Thank you so much. Keep up the good job.
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