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An Introduction to Statistical Learning with Applications in Python

statlearning.com

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Re: An Introduction to Statistical Learning with Applications in Python

#41

Post by graycat »

laichzeit0 wrote: post #18
graycat wrote: post #16

Might consider, say, Sidney Siegel, N. John Castellan, Jr. 'Nonparametric Statistics for the Behavioral Sciences, Second Edition', ISBN 0-07-057357-3, McGraw-Hill, New York, 1988. So, "nonparametric" means make no assumptions about a probability distribution based on parameters . Or, call the material distribution-free . E.g., get to see about resampling plans -- tiny assumptions, really simple, darned cleaver, quite…

Huh? I have that book, and it's nothing like ISLR, at all. It's a good book, but ISLR covers topics such as gradient boosted trees, survival analysis, GLMs, etc. Nothing at all like the book you mentioned. If forced, you could say ISLR is more focused on prediction, not inference or hypothesis testing.

For statistical learning, the book I mentioned

Nonparametric Statistics for the Behavioral Sciences,

should make a good contribution to statistical learning. Some of the techniques are so robust, i.e., need such meager assumptions, that they should be especially welcome in automatically applied AI (artificial intelligence) applications.

Actually the book ISLR, Introduction to Statistical Learning, does claim to cover

"Resampling methods"

Re: An Introduction to Statistical Learning with Applications in Python

#42

Post by A4ET8a8uTh0 »

TheAlchemist wrote: post #10

Off topic, but it's very interesting to observe the ratio of upvotes / comments. On any given chatGPT topic, there are hundreds of comments usually. Here, so far 100 upvotes, only 7 comments. The book looks great - and given the authors, it almost certainly is (I will buy it for sure). It makes me think though about the state of 'ML / AI / Data Science' - and the cynic part of me thinks that this upvotes / comments r…

There is, naturally, a reason for this. GPT is effectively a nice wrap on an otherwise complicated set of issues. I almost think of it as gui instead of console. Yeah, you lose some of the functionality and control, but a lot of people will take it and run with it simply because it is just so much easier.

Case in point, Google AML AI, which promises to do away with pesky model validation and such ( because it will do everything in a closed box you will not have a reason to investigate ). I am already looking forward to the conversations with regulators.

Re: An Introduction to Statistical Learning with Applications in Python

#43

Post by posterboy »

adamsmith143 wrote: post #37
TheAlchemist wrote: post #10

Off topic, but it's very interesting to observe the ratio of upvotes / comments. On any given chatGPT topic, there are hundreds of comments usually. Here, so far 100 upvotes, only 7 comments. The book looks great - and given the authors, it almost certainly is (I will buy it for sure). It makes me think though about the state of 'ML / AI / Data Science' - and the cynic part of me thinks that this upvotes / comments r…

This is pure gatekeeping. The math behind LLMs, that is, the math behind Neural Nets, is undergrad freshman level Calculus and some linear algebra. Not really complex at all. Can you deal with derivatives, the chain rule and matrix multiplications? Great you know all the "math" behind Deep Learning.

> Can you deal with derivatives

Linear Algebra is a huge ... space?

And then there's all the other algebras. I'm not sure what you are thinking, like, do you not know matrix multiplication you had better get a clue.

Re: An Introduction to Statistical Learning with Applications in Python

#44

Post by tndoan »

KRAKRISMOTT wrote: post #3
srvmshr wrote: post #2

This is an update to a very popular text which was originally in R. Professors Hastie & Tibshirani are leading educators in statistical learning. They also have a video course following these notes in Stanford Online. Very highly recommended if learning theoretical aspects of classical ML

Any plans for a Julia version?

Honestly, I dont think they will have Julia version soon. I converted all the code in this book to Julia sometimes ago:

https://github.com/tndoan/ISLR.jl

Re: An Introduction to Statistical Learning with Applications in Python

#45

Post by StefanWestfal »

defphysics wrote: post #20

Personally, I prefer Bishop (Pattern Recognition and Machine Learning) or Murphy (Probabilistic Machine Learning: An Introduction).

I heard good things about Bishop however I am a SE that would like do know more about what the ML team is doing and maybe work on some ML side projects. Would you recommend Bishop here or is it considerer to theoretical for such a case?

Re: An Introduction to Statistical Learning with Applications in Python

#46

Post by rossdavidh »

wodenokoto wrote: post #31

I’ve been meaning to do a comparison of lab zero between the two. I’ve only had the chance to look over the Python lab for a few minutes, but compared to what I remember from the R labs, it is much, much more involved and longer. I know HN likes to complain about how difficult and confusing R is, but I think that it is an easier language for beginners or stat inclined people to start doing statistical work in. /two c…

Python is more natural for programmers learning statistics/ML. R is more natural for statisticians learning programming. Which is not too surprising, since those were the audiences each language was intended for. I think it's good to be accepting of both, and use the one that works better for a given task.

Re: An Introduction to Statistical Learning with Applications in Python

#47

Post by rossdavidh »

semajian wrote: post #14

A bit too wordy for me, I wish Landau was around to rewrite books like these, they'd be much shorter. Can we get a LLM to make it happen?

Sure, as long as it's ok if it makes up stuff that isn't true a few times per chapter. :)

Re: An Introduction to Statistical Learning with Applications in Python

#49

Post by Exuma »

srvmshr wrote: post #2

This is an update to a very popular text which was originally in R. Professors Hastie & Tibshirani are leading educators in statistical learning. They also have a video course following these notes in Stanford Online. Very highly recommended if learning theoretical aspects of classical ML

For anyone looking for the videos: https://www.youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQy...

Re: An Introduction to Statistical Learning with Applications in Python

#50

Post by suchagain »

melling wrote: post #19
TheAlchemist wrote: post #10

Off topic, but it's very interesting to observe the ratio of upvotes / comments. On any given chatGPT topic, there are hundreds of comments usually. Here, so far 100 upvotes, only 7 comments. The book looks great - and given the authors, it almost certainly is (I will buy it for sure). It makes me think though about the state of 'ML / AI / Data Science' - and the cynic part of me thinks that this upvotes / comments r…

It’s a well-known machine learning book. I’ve read through it and done the exercises in R. https://github.com/melling/ISLR There’s an edX course from the authors: https://www.edx.org/course/statistical-learning

I also went through this book for a GIS course in grad school and put together some Python notebooks. https://github.com/gbrunner/islr-for-gis
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