5 ms·

Model-Based Machine Learning Book

365 points by r0f1 8y ago

formalsystem 8y ago
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.
- compcoffee 8y ago
  >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?
  - darkmighty 8y ago
    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_Information_Theory_Elements.pdf http://www.cs-114.org/wp-content/uploads/2015/01/Elements_of... 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.
    - deleted 8y ago
      [deleted]
  - formalsystem 8y ago
    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-Python/dp/149190142X/ref=sr_1_4?s=books&ie=UTF8&qid=1539500623&sr=1-4&keywords=data+science+from+scratch 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-Springer/dp/1441923225 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/ 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-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 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.
dang 8y ago
Discussed in 2016: https://news.ycombinator.com/item?id=13099698 https://news.ycombinator.com/item?id=13099698
mlazos 8y ago
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.
amrx431 8y ago
Uses this https://dotnet.github.io/infer/ https://dotnet.github.io/infer/. Would have been easier with some Python packages.
- mooneater 8y ago
  which ones would you suggest? pgmpy?
metakermit 8y ago
Looks really well written – the fun & practical examples look very inviting.
tomrod 8y ago
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.
bad_news_bears 8y ago
Am I reading that site correctly, that the full book has not yet been released? Does anyone know of a timeline for it?
- ChristianGeek 8y ago
  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.

365 points by r0f1 8y ago

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.

compcoffee 8y ago
>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?
- darkmighty 8y ago
  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_Information_Theory_Elements.pdf http://www.cs-114.org/wp-content/uploads/2015/01/Elements_of... 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.
  - deleted 8y ago
    [deleted]
- formalsystem 8y ago
  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-Python/dp/149190142X/ref=sr_1_4?s=books&ie=UTF8&qid=1539500623&sr=1-4&keywords=data+science+from+scratch 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-Springer/dp/1441923225 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/ 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-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 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.

dang 8y ago

Discussed in 2016: https://news.ycombinator.com/item?id=13099698 https://news.ycombinator.com/item?id=13099698

mlazos 8y ago

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.

amrx431 8y ago

Uses this https://dotnet.github.io/infer/ https://dotnet.github.io/infer/. Would have been easier with some Python packages.

mooneater 8y ago
which ones would you suggest? pgmpy?

metakermit 8y ago

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

tomrod 8y ago

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.

bad_news_bears 8y ago

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

ChristianGeek 8y ago
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.