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Model-Based Machine Learning

mbmlbook.com

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Re: Model-Based Machine Learning

#11

Post by rm999 »

brudgers wrote: post #9
rm999 wrote: post #7

Earlier quoted context omitted.

It was an honest question, not snark (passive aggressiveness is not my style). The introduction is kind of hidden on the page, and clarifies the meaning of "model" in this context. Otherwise, GP is correct that "model" is often used to mean a supervised model, and that people generally call it "supervised learning", not "model-based learning".

I'm glad it was an honest question. Editing the comment is an option. I think the guideline exists because even as an honest question it does not add anything to the comment and at best an answer doesn't change anything and at worst it detracts from meaningful dialog. One feature of this particular guideline is that it provides an alternative phrasing that is likely to avoid misinterpretation.

>I think the guideline exists because even as an honest question it does not add anything to the comment

I hope you see the irony here considering how much you're derailing this conversation (I'm only responding because I realize your intentions are good). And I'm pretty confident my comment added plenty of value to the discussion - I realize sometimes tone is lost in text, but after my clarification I don't see why you need to harp on this. Anyway, original comment edited.

Re: Model-Based Machine Learning

#12

Post by kristjankalm »

yummyfajitas wrote: post #4
nkozyra wrote: post #2

I've never heard supervised learning referred to as model-based learning.

This isn't a book about supervised learning, from what I can tell. Based on my reading of the murder mystery and the skill assessment, it's about defining models based on your understanding of the underlying system and then fitting them to the data. This is a lot closer to classical statistics than machine learning.

rather than downvoting i'm actually curious why you think unsupervised learning is not ML?

there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI

Re: Model-Based Machine Learning

#13

Post by brudgers »

rm999 wrote: post #11
brudgers wrote: post #9

Earlier quoted context omitted.

I'm glad it was an honest question. Editing the comment is an option. I think the guideline exists because even as an honest question it does not add anything to the comment and at best an answer doesn't change anything and at worst it detracts from meaningful dialog. One feature of this particular guideline is that it provides an alternative phrasing that is likely to avoid misinterpretation.

>I think the guideline exists because even as an honest question it does not add anything to the comment I hope you see the irony here considering how much you're derailing this conversation (I'm only responding because I realize your intentions are good). And I'm pretty confident my comment added plenty of value to the discussion - I realize sometimes tone is lost in text, but after my clarification I don't see why…

If I had thought of suggesting editing your comment before posting my second comment, then it might have been different. And in a similar situation in the future I well might. That said, until I thought about it a bit more, it didn't occur to me. Anyway, for me, writing is thinking.

Re: Model-Based Machine Learning

#14

Post by jamessb »

ThePhysicist wrote: post #3

I have to say the layout of this website looks great! Very accessible and clean. Was it made with a specific framework?

One of the css files [0] includes a copyright notice for Skeleton ("A dead simple, responsive boilerplate"). [1].

[0]: http://mbmlbook.com/HtmlReader.styles.base.css

[1]: http://getskeleton.com/

Re: Model-Based Machine Learning

#15

Post by danappelxx »

jamessb wrote: post #14
ThePhysicist wrote: post #3

I have to say the layout of this website looks great! Very accessible and clean. Was it made with a specific framework?

One of the css files [0] includes a copyright notice for Skeleton ("A dead simple, responsive boilerplate"). [1]. [0]: http://mbmlbook.com/HtmlReader.styles.base.css [1]: http://getskeleton.com/

Hmm, not very responsive for me (iPhone 6 safari iOS 10)

Re: Model-Based Machine Learning

#16

Post by huac »

kristjankalm wrote: post #12
yummyfajitas wrote: post #4

Earlier quoted context omitted.

This isn't a book about supervised learning, from what I can tell. Based on my reading of the murder mystery and the skill assessment, it's about defining models based on your understanding of the underlying system and then fitting them to the data. This is a lot closer to classical statistics than machine learning.

rather than downvoting i'm actually curious why you think unsupervised learning is not ML? there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI

I think yummyfajita's point is that a traditional statistics approach begins with some understanding of the system being modeled, and that you create a model using that understanding. There is usually a high focus on parsimoniousness and explainability, while in ML/AI, you don't really care what the underlying model is or how the model comes to a particular conclusion. The focus is on accuracy at the expense of explainability.

Re: Model-Based Machine Learning

#17

Post by povik »

kristjankalm wrote: post #12
yummyfajitas wrote: post #4

Earlier quoted context omitted.

This isn't a book about supervised learning, from what I can tell. Based on my reading of the murder mystery and the skill assessment, it's about defining models based on your understanding of the underlying system and then fitting them to the data. This is a lot closer to classical statistics than machine learning.

rather than downvoting i'm actually curious why you think unsupervised learning is not ML? there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI

I am afraid you misunderstood. OP's point isn't that it's unsupervised learning, hence not ML. His point is that it's no learning at all. IMHO.

Re: Model-Based Machine Learning

#18

Post by yummyfajitas »

kristjankalm wrote: post #12
yummyfajitas wrote: post #4

Earlier quoted context omitted.

This isn't a book about supervised learning, from what I can tell. Based on my reading of the murder mystery and the skill assessment, it's about defining models based on your understanding of the underlying system and then fitting them to the data. This is a lot closer to classical statistics than machine learning.

rather than downvoting i'm actually curious why you think unsupervised learning is not ML? there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI

To me at least, the major distinction between "classical statistics" and "machine learning" is that machine learning" strives to work independently of the underlying distribution while classical statistics tries to model it.

I.e., a statistician doing linear regression assumes that reality is linear (or at least differentiable) in the region of interest. A convergence proof of linear regression will use this assumption.

A machine learning practitioner does NOT assume reality actually has a random forest out there in the world somewhere, and as a result needs to prove far more general (and less accurate) convergence results for the random forest.

From what I can tell, this book falls into the former category.

Re: Model-Based Machine Learning

#20

Post by xapata »

yummyfajitas wrote: post #18
kristjankalm wrote: post #12

Earlier quoted context omitted.

rather than downvoting i'm actually curious why you think unsupervised learning is not ML? there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI

To me at least, the major distinction between "classical statistics" and "machine learning" is that machine learning" strives to work independently of the underlying distribution while classical statistics tries to model it. I.e., a statistician doing linear regression assumes that reality is linear (or at least differentiable) in the region of interest. A convergence proof of linear regression will use this assumpti…

> assumes that reality is linear

The assumption is that a particular relationship is reasonable to model as if it were linear. No one believes reality is strictly linear.

I've read your posts enough to believe you know how linear regression works. I'm criticizing your comment because it encourages a misunderstanding of traditional statistics as having nonsensical assumptions.

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