
Model-Based Machine Learning Book - r0f1
http://mbmlbook.com/
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formalsystem
As someone who has read pretty much every single applied ML and theoretical ML
book out there, I'm extremely excited about this.

The theoretical books tend to go over a laundry list of techniques without
much clear motivation. The applied books often just take a single technique
and write the code for it.

This book, on the other hand, is teaching you how to think like a statistician
to solve some nontrivial analytical problems. Not many new books even cover
this skillset, so you'll most likely be learning a lot of net new things.

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compcoffee
> _As someone who has read pretty much every single applied ML and theoretical
> ML book out there_

Would you mind recommending a couple of your favorites in both the theoretical
and applied?

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darkmighty
If you're looking for a bit of an unconventional entry point, I recommend the
seminal text 'Elements of Information Theory' by T. Cover (skipping chapters
like Network Information/Gaussian channel should be fine), paired with David
MacKay's 'Information Theory, Inference and Learning Algorithms'. Both seem
available online:

[http://www.cs-114.org/wp-
content/uploads/2015/01/Elements_of...](http://www.cs-114.org/wp-
content/uploads/2015/01/Elements_of_Information_Theory_Elements.pdf)

[http://www.inference.org.uk/itprnn/book.pdf](http://www.inference.org.uk/itprnn/book.pdf)

They cover some fundamentals of what optimal inference looks like, why current
methods work, etc (in a very abstract way by understanding Kolmogorov
complexity and its theorems and in a more concrete way in MacKay's text).
Another good theoretical partner could be the 'Learning from data' course, yet
a little more applied: (also available for free)

[https://work.caltech.edu/telecourse.html](https://work.caltech.edu/telecourse.html)

Excellent lecturer/material (to give a glimpse, take lecture 6: 'Theory of
Generalization -- how an infinite model can learn from a finite sample').

Afterward I would move to modern developments (deep learning, or whatever
interests you), but you'll be well equipped.

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dang
Discussed in 2016:
[https://news.ycombinator.com/item?id=13099698](https://news.ycombinator.com/item?id=13099698)

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amrx431
Uses this [https://dotnet.github.io/infer/](https://dotnet.github.io/infer/).
Would have been easier with some Python packages.

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mooneater
which ones would you suggest? pgmpy?

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mlazos
I really enjoyed doing some of the problems in this book. I worked on a team
at Microsoft that used the inbox clutter algorithm in production and it was
illuminating to see a real life application of graphical models.

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bad_news_bears
Am I reading that site correctly, that the full book has not yet been
released? Does anyone know of a timeline for it?

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ChristianGeek
It’s been “Early Access” for almost two years and they’ve completed 6/9
sections according the current TOC (which implies there may be more than 9) if
that tells you anything.

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metakermit
Looks really well written – the fun & practical examples look very inviting.

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tomrod
Well done to the authors. I am deep in this space. This book looks to be solid
on my skim of TOC and chapter 2.

