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Probabilistic Machine Learning: An Introduction

probml.github.io

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Re: Probabilistic Machine Learning: An Introduction

#31

Post by amitport »

SideQuark wrote: post #22
jbay808 wrote: post #15

Earlier quoted context omitted.

It's very encouraging to see Matlab losing ground in the educational space. I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that.

>I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that. Because no open source toolkit can do what Matlab can do. The same is true of a lot of high end software: Photoshop, pretty much any serious parametric CAD modeling system (say, SolidWorks), DaVinci Resolve, Ableton Live, etc. When a professional costs $100K+ to employ, paying a few grand to make t…

As someone who helped migrate a university course from Matlab to python I must say proprietary features of Matlab had nothing to do with the reason it lasted so long.

Basically, it was mainly inhertia. Older professors that liked it and rarly used anything else and the fact that generally no one gets rewarded for actually rewriting parts of an existing functioning course.

As an instructor you basically create more work for yourself in the first time you migrate a course's programming language. (And you also annoy some senior staff when forcing them to learn new things)

Re: Probabilistic Machine Learning: An Introduction

#32

Post by joshvm »

sjg007 wrote: post #29
jaredtn wrote: post #6

Quite excited to read this. Murphy does a great job of explaining concepts from first principles.

I think it would be extremely helpful to map the math into code. Nobody has done this as far as I've seen. I mean you can find Github repositories for some papers but really a set of explicit tutorials from the math to the code would be really helpful. To say something is "machine learning", I think means that you should show the code not just equations and derivations. I mean if you only show math and derivations, w…

This exists actually, it's not complete yet (I think?) but it covers a lot of the material in the book:

https://github.com/probml/pyprobml

Re: Probabilistic Machine Learning: An Introduction

#34

Post by martingoodson »

master_yoda_1 wrote: post #16
hnracer wrote: post #11

Earlier quoted context omitted.

Are you saying the book itself is ML porn?

Yes, unless you are among 20 top researchers who are working on frontier of ml. Bayesian probabilistic techniques does not work or are very slow for any practical purpose.

This may be true for whatever small corner of the data science world you inhabit but it isn’t true in general.

To choose just one example, the analysis of the new UK COVID variant relies on Bayesian modelling, both for the government analysis and the Imperial paper. (https://www.imperial.ac.uk/media/imperial-college/medicine/m...)

Re: Probabilistic Machine Learning: An Introduction

#35

Post by jbay808 »

SideQuark wrote: post #22
jbay808 wrote: post #15

Earlier quoted context omitted.

It's very encouraging to see Matlab losing ground in the educational space. I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that.

>I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that. Because no open source toolkit can do what Matlab can do. The same is true of a lot of high end software: Photoshop, pretty much any serious parametric CAD modeling system (say, SolidWorks), DaVinci Resolve, Ableton Live, etc. When a professional costs $100K+ to employ, paying a few grand to make t…

I guess I'll rephrase - if you can't understand a transfer function or a probability distribution without opening Matlab, then you've allowed your own expertise to be held hostage. Unfortunately, I know a large number of professionals for whom this is true.

If you're more productive in Matlab, that's fine. But if you're at a loss without it, that's not.

It doesn't belong in the education system or in educational books.

Re: Probabilistic Machine Learning: An Introduction

#37

Post by sgt101 »

master_yoda_1 wrote: post #16
hnracer wrote: post #11

Earlier quoted context omitted.

Are you saying the book itself is ML porn?

Yes, unless you are among 20 top researchers who are working on frontier of ml. Bayesian probabilistic techniques does not work or are very slow for any practical purpose.

Oh crumbs! There I was thinking that by obtaining an estimate of the probabilities of the responses of different groups to an employee survey I was applying a bayesian probalistic approach.

I'm going to have to rethink everything now as since it worked and was quite quick (I didn't even sample using MCMC, just brute force pulled permutations) so it was clearly not a bayesian approach, and I am very very far from one of the top 20 (or 200, or 2000 or 20000, maybe 200000?) researchers...

Re: Probabilistic Machine Learning: An Introduction

#39

Post by abhgh »

SideQuark wrote: post #22
jbay808 wrote: post #15

Earlier quoted context omitted.

It's very encouraging to see Matlab losing ground in the educational space. I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that.

>I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that. Because no open source toolkit can do what Matlab can do. The same is true of a lot of high end software: Photoshop, pretty much any serious parametric CAD modeling system (say, SolidWorks), DaVinci Resolve, Ableton Live, etc. When a professional costs $100K+ to employ, paying a few grand to make t…

In the specific case of ML courses - many of which I have TA-ed or attended classes of, this reason does not ring true at all. Libraries for most standard algorithms are available in some form with a Python interface (or for the more statistical stuff: R). Its almost always the inertia from the initial design of the course.

It is also not true today that not knowing Matlab harms your industry productivity in ML. This might have been true around a decade ago, but most teams outside academia also have moved to non-Matlab resources. And if anything, this has been further reinforced by Deep Learning libraries, the current crop of MLOps tools and cloud-based frameworks.

Matlab might be good for specific areas, but ML has not been a stronghold for a while. It is also important to remember that in the context of numerical accuracy or computation speed, Python is almost always the user-facing layer. You might (correctly) argue that the Python language is slower/faster than X, but this is not a useful metric for comparing libraries and frameworks, where the compute heavy code is probably in C/C++: numpy, tensorflow, pytorch are good examples of this.

Re: Probabilistic Machine Learning: An Introduction

#40

Post by mark_l_watson »

dhairya wrote: post #13

also recommend probabilistic methods for hackers as another resource to explore this space: https://camdavidsonpilon.github.io/Probabilistic-Programming...

Thanks, that looks like a good resource for my learning style. I usually learn new things by playing around with code before diving into theory (I know that this sounds backwards).
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