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Introduction to Statistical Learning, with Applications in R
- Adam_O 11y agoI have been waiting for this session to begin for a few months now (the course ran just once in 2015). I'm planning to try the exercises in python too after doing them in R, reference material for this exists already: https://github.com/sujitpal/statlearning-notebooks https://github.com/sujitpal/statlearning-notebooks
- minimaxir 11y agoIf you've followed my posts on Hacker News, you may notice I'm a HUGE user of R. I actually started using R because the classes I took as a statistics minor in college required the use of R. And it sucked, mostly because trying to reimplement statistical algorithms can be very fussy (it's not as well suited toward linear algebra as MATLAB and Octave) I note that because the curriculum seems to focus on more machine-leaning-esque algorithms like Random Forests and SVM, which are heavy in linear algebra, but the class itself states it is not math heavy. In R, you typically implement these algorithms...by installing a package from CRAN that contains the algorithm. (I skimmed through the linked book at that appears to be the case) Nothing wrong with that, of course. Although a more proper intro to R would require teaching how to use the factor data type without going insane. (Processing data is half the battle, and something I wish I learned at college)
- carbocation 11y agoThe difference between writing software (including machine learning, but a ton of useful software has to lunge data of some sort of another) and learning programming reminds me of the difference between learning medicine in school and then learning how to actually practice medicine in residency.
- IndianAstronaut 11y agoTo add to that, many of the practical data problems I run into almost never use most of the techniques you learn in classes. Either it is a small subset which can be more robust in data handling, or it is a technique or package not covered in classwork.
- hadley 11y agoOr just avoid using factors in the first place. Character vectors are usually more appropriate and a modern ingest pipeline won't produce factors in the first place.
- minimaxir 11y agoThanks to readr's stringsAsFactors=F default, I've fortunately been avoiding unexpected issues due to factoring. However, the default R functions still load data as factors and I have bad memories of that catching me by surprise, particularly when performing mathematical operations on vectors which are secretly factors and silently getting wrong results. I still need to use factors with ggplot2 though for discrete orders. (E.g bar chart sorting)
- hadley 11y agoYeah I've been thinking about making a new class based on top of character vectors that allows custom ordering
- rabboRubble 11y agoAfter much frustration with a project I'm working on, yesterday I stumbled across using data.frame instead of cbind. Are you maybe Hadley Wickham? If yes, you probably already know this :-) My brother bought me "Advanced R" for Christmas. edit: data.frame, not read.table
- washedup 11y agoSo glad that stringsAsFactors exists. Saves me a lot of time.
- washedup 11y agoCouldn't agree more. New users I help often get stuck when they run into factors. Skip the whole class! Strings are the way to go.
- baldfat 11y agoreadr - Doesn't place factors in the first place. R reader written by Hadley Wickham https://cran.r-project.org/web/packages/readr/README.html https://cran.r-project.org/web/packages/readr/README.html
- washedup 11y agoVery helpful course, I highly recommend it. If nothing else, get a copy of the book: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.... Even though the book is introductory material, it explains everything with great clarity. I have yet to find a better resource for someone wanting to get into data science. The theoretical material side-by-side with coded examples is very powerful (although the labs could use some work). Even if you have been using data science for a while, this is a great refresher on theoretical concepts.
- dcre 11y agoLatest printing (only difference is corrections, I believe): http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.p...
- jajaBinks 11y agoFor someone interested in neural networks, deep learning and stuff like that, is this a useful course? Also, how valuable a skill-set is R?
- washedup 11y agoThere is not much here about deep learning or neural networks, but from a "how to measure and rate models" perspective, it is very helpful. I consider R to be a very useful skill-set. I have used it for solving many issues, both in and out of stats.
- phillipamann 11y agoThis course is more of an introduction to statistics.
- screwedup 11y agoNo, it's an introduction to Machine Learning (without much neural network stuff).
- bcaine 11y agoTwo courses I've found are very good for Neural Networks & Deep Learning are Karpathy's CS231n from Stanford[0] and Nando de Freitas's Deep Learning class from Oxford[1][2]. Been through parts of both and they seem really good. That said, I'd bet that both of these classes would be a lot more meaningful after either Hastie's class (listed here) or Andrew Ng's course. [0] http://cs231n.github.io/ http://cs231n.github.io/ [1] https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPo... [2] http://www.cs.ox.ac.uk/teaching/courses/2014-2015/ml/ http://www.cs.ox.ac.uk/teaching/courses/2014-2015/ml/
- nl 11y agoFor someone interested in neural networks, deep learning and stuff like that, is this a useful course? Not really (depending on exactly what "stuff like that" means) Also, how valuable a skill-set is R? Very
- phillipamann 11y agoI took this course last year. I had a degree in statistics already. I just wanted to go through this GREAT book and watch these great professors. If you have any interest in learning actual statistics, this is a great introduction. Again, I can't repeat this enough but both the book and the professors are great!
- farhanhubble 11y agoThank you @phillipamann. I am currently overwhelmed by the number of great MOOCs starting in Jan 2016 and wanted to know if this one would be worth it.
- cossatot 11y agoDaniela Witten, a coauthor on the course textbook, has a couple very interesting videos on statistical learning with graphical models of cancer genetics. A good one is here: https://www.youtube.com/watch?v=jmnJiXA5fm0 https://www.youtube.com/watch?v=jmnJiXA5fm0
- washedup 11y agoThanks for sharing, great videos.
- satyajeet23 11y agoThanks for this!
- fizixer 11y agoThis text misses two very important sub-areas of statistical learning, so much so that I can't believe they gave their textbook such a broad name ("statistical learning", more like a subset of it. The whole book doesn't have a single mention of the word "markov". Seriously disappointed.). - Graphical models - Probabilistic logic, probabilistic programming, bayesian data analysis, etc, etc. Can anyone point to good introductory texts? I know for the first, Koller's text is highly recommmended but I'm not sure if it's at the introductory level.
- Aqwis 11y agoAs someone in the field, I don't think there are any texts at below the level of Koller/Friedman. Universities rarely if ever teach graphical models to students below the level at which Koller/Friedman is appropriate.
- platz 11y agoIm not sure graphical models should be the default tool people reach for
- IndianAstronaut 11y agoImplementation of these graphical models in R is dkne really well with the gRain package.
- dragandj 11y agoKruschke - Doing Bayesian Data Analysis (2nd ed)
- Eridrus 11y agoThere's a coursera course on Probabilistic Graphical Models: https://www.coursera.org/course/pgm https://www.coursera.org/course/pgm I would guess that's more approachable than a text book, but who knows.
- cs702 11y agoThe lecturers here, Hastie and Tibshirani, are also the authors of the classic text book, "Introduction to Statistical Learning," probably the best introduction to machine/statistical learning I have ever read.[1] I highly recommend the book and this online course, both of which are FREE. Hastie and Tibshirani's other book, "The Elements of Statistical Learning," is also excellent but far more theoretical, and best for experienced practicioners who want to use it as a reference guide.[2] -- [1] http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.p... [2] http://statweb.stanford.edu/~tibs/ElemStatLearn/ http://statweb.stanford.edu/~tibs/ElemStatLearn/
- argonaut 11y agoEven if all you're interested in is deep learning, let me just say that the topics covered in this are absolutely vital if you're to be taken seriously as a machine learning practitioner.
- graycat 11y agoThis post has a critical side. Some readers will be offended. But most readers should be glad that they got some hype peeled back and learned a little more. Sounds like an updated course in otherwise just mostly classic multivariate statistics. I have about four feet of such on my bookshelf. Okay, have some more recent versions of regression and Breiman's work on classification and regression trees and random forests and some resampling. Right. Okay. Fine. Maybe as good as multivariate statistics has been for decades, maybe a little better. Fine. They are using R instead of, say, SPSS, SAS, or the old IBM SSL? Okay. Been doing that stuff since early in my career, have published in essentially multi-variate resampling, and am currently using some derivations I did for a modified, generalized version of some of that material. Fine. All that said, where did this stuff about learning enter? This is nearly all old stuff; where'd this learning stuff come from? Getting estimates of parameters? Sure. Estimates that are minimum variance and unbiased? If can, sure -- good stuff. Confidence intervals on the estimates? Of course. Statistical hypothesis tests of significance -- standard stuff. A variance-covariance matrix, symmetric positive definite? Sure. Polar decomposition, non-negative eigenvalues, a basis of orthogonal eigenvectors, singular value decomposition, matrix condition number, ..., definitely. But learning? Where'd that come from? Learning sounds like an attempt, like neural networks, artificial intelligence, deep learning to suggest that this work is somehow close to human thinking, which, Virginia, I have to guarantee you it definitely is not. They didn't get learning from statistics. Okay, maybe they got learning from computer science. So sounds like statistics just stole learning from computer science or computer science stole learning from psychology and stole all the rest from statistics. Academic theft? Old wine in new bottles with a new label, "Learning"? Add mud to the waters, stir rapidly and confuse people? Hype? Sell books? Get students? Get consulting gigs? Get grants? Get publicity? Doesn't look good. Students trying to learn: For the classic multivariate statistics, there are stacks of highly polished texts. For more on closely related statistics, look at the work of Leo Breiman. For hypothesis testing, that is nearly all classic Statistics 101. For resampling, look at the work of P. Diaconis at Stanford or B. Efron at Yale. In that way, you will have mostly highly polished, classic sources. Econometrics has made a lot of use of this material. Also look at analysis of variance, which is closely related and quite powerful and heavily used in some fields of experimental science, e.g., agriculture. For "learning", mostly f'get about it.