Model-Based Machine Learning Book(mbmlbook.com)
mbmlbook.com
Model-Based Machine Learning Book
http://mbmlbook.com/
7 comments
Am I reading that site correctly, that the full book has not yet been released? Does anyone know of a timeline for it?
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.
Looks really well written – the fun & practical examples look very inviting.
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.
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.
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.
>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?
Would you mind recommending a couple of your favorites in both the theoretical and applied?
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.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
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.
http://www.cs-114.org/wp-content/uploads/2015/01/Elements_of...
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
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.
[deleted]
Feel free to PM if you need more specific recommendations since it was really hard to try and come up with a concise list. I've seen a lot of friends and colleagues struggle with some specific popular books. Sometimes its OK not to like the way someone writes even if they're really smart and super famous.
My two favorite applied ones are
* Data science from scratch because it's one of the most concise and logical expositions of most ML algorithms in simple python that you'll remember and be able to reproduce from scratch if need be https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...
* Deep learning with python as a next step since it covers more complicated neural net architectures using Keras so not from scratch but with clear code that you'll again remember
* Designing data-intensive apps because it'll prepare you for most challenges you'll face as a data engineer
On the theoretical side
* All of statistics: it's been recommended here on hacker news many times and for good reason. Its scope is very ambitious and it avoids the trap that math books fall into of leaving too many seriously hard steps an exercise to the reader. https://www.amazon.com/All-Statistics-Statistical-Inference-...
* Convex optimization which will give you the theoretical foundation to understand mathematically supervised and unsupervised learning http://web.stanford.edu/~boyd/cvxbook/
I'll also add a reference to a Reinforcement Learning resource because I'm trying to build a game AI company using its ideas. Simple RL with TF because being able to program virtual robots to do stuff is really cool and this is one of the easier ways to better grasp RL https://medium.com/emergent-future/simple-reinforcement-lear.... Probably worth studying in conjunction with RL an introduction which is more theoretical and has a Q&A like approach to understanding the material which was interesting.
My two favorite applied ones are
* Data science from scratch because it's one of the most concise and logical expositions of most ML algorithms in simple python that you'll remember and be able to reproduce from scratch if need be https://www.amazon.com/Data-Science-Scratch-Principles-Pytho...
* Deep learning with python as a next step since it covers more complicated neural net architectures using Keras so not from scratch but with clear code that you'll again remember
* Designing data-intensive apps because it'll prepare you for most challenges you'll face as a data engineer
On the theoretical side
* All of statistics: it's been recommended here on hacker news many times and for good reason. Its scope is very ambitious and it avoids the trap that math books fall into of leaving too many seriously hard steps an exercise to the reader. https://www.amazon.com/All-Statistics-Statistical-Inference-...
* Convex optimization which will give you the theoretical foundation to understand mathematically supervised and unsupervised learning http://web.stanford.edu/~boyd/cvxbook/
I'll also add a reference to a Reinforcement Learning resource because I'm trying to build a game AI company using its ideas. Simple RL with TF because being able to program virtual robots to do stuff is really cool and this is one of the easier ways to better grasp RL https://medium.com/emergent-future/simple-reinforcement-lear.... Probably worth studying in conjunction with RL an introduction which is more theoretical and has a Q&A like approach to understanding the material which was interesting.
Discussed in 2016: https://news.ycombinator.com/item?id=13099698
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.
Uses this https://dotnet.github.io/infer/. Would have been easier with some Python packages.
which ones would you suggest? pgmpy?