Model-Based Machine Learning
mbmlbook.com
Model-Based Machine Learning
1–10 of 31 posts
Re: Model-Based Machine Learning
#2I've never heard supervised learning referred to as model-based learning.
Re: Model-Based Machine Learning
#3I have to say the layout of this website looks great! Very accessible and clean. Was it made with a specific framework?
Re: Model-Based Machine Learning
#4I'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.
Re: Model-Based Machine Learning
#5I've never heard supervised learning referred to as model-based learning.
The introduction clarifies what the authors mean. In this context "model" isn't about implementing a supervised model, it's about "modeling" your problem to build a bespoke algorithm that closely matches the problem. Unsupervised methods like clustering would probably fit in here too.
I haven't read much of this early access book yet, but I'd give the authors a lot of benefit of the doubt. Christopher Bishop wrote one of my favorite machine learning books (I read it after my graduate study in machine learning and it filled in a lott of the gaps): https://www.amazon.com/Pattern-Recognition-Learning-Informat...
Re: Model-Based Machine Learning
#6I've never heard supervised learning referred to as model-based learning.
The introduction clarifies what the authors mean. In this context "model" isn't about implementing a supervised model, it's about "modeling" your problem to build a bespoke algorithm that closely matches the problem. Unsupervised methods like clustering would probably fit in here too. I haven't read much of this early access book yet, but I'd give the authors a lot of benefit of the doubt. Christopher Bishop wrote on…
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Please don't insinuate that someone hasn't read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that."
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Re: Model-Based Machine Learning
#7Earlier quoted context omitted.
The introduction clarifies what the authors mean. In this context "model" isn't about implementing a supervised model, it's about "modeling" your problem to build a bespoke algorithm that closely matches the problem. Unsupervised methods like clustering would probably fit in here too. I haven't read much of this early access book yet, but I'd give the authors a lot of benefit of the doubt. Christopher Bishop wrote on…
From the Hacker News guidelines: Please don't insinuate that someone hasn't read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that." It is possible to edit the comment to remove the phrase if you wish.
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".
Re: Model-Based Machine Learning
#8I've never heard supervised learning referred to as model-based learning.
As rm999 points out, this book is vastly more useful than limiting distinctions such as supervised/unsupervised learning (what happens in brains is learning while predicting, which is not completely well captured by that delineation, nor even fully by reinforcement learning).
This book will provide a set of skills which will age far better than if it had been specific to some machine learning framework or ideas. It's one of the best I've seen on reasoning probabilistically, bayesian networks, graphical models and probabilistic programming generally. It also teaches the core of the involved algorithms. These skills will be important going forward as we seek to implement ever more brain like systems (and better). The knowledge will also carry over to gaussian processes (which are a subset really) and the more future proof generative deep learning ideas.
It also teaches how to reason about your problem and diagnose machine learning systems. Whether you're designing features, trying to figure out how to make a research paper work in real life, or are one of the rare people capable of coming up with deep learning architectures, what the book teaches will be indispensable to you.
Re: Model-Based Machine Learning
#9Earlier quoted context omitted.
From the Hacker News guidelines: Please don't insinuate that someone hasn't read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that." It is possible to edit the comment to remove the phrase if you wish.
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.
Re: Model-Based Machine Learning
#10I've never heard supervised learning referred to as model-based learning.
My take from the introduction is that the books is going to mostly be about probabilistic graphical models (PGMs).
I look forward to reading this book when finished and hope they find success with this presentation of the core ideas. As a practitioner I see a fair amount of "I have a hammer; now I just need this problem to be a nail" type thinking with regard to using off-the-shelf techniques.
In the intro to this book the authors have an example with Kalman filters. A similar example is how Latent Dirichlet Allocation (LDA) is treated by different communities. In a certain chunk of the CS-dominated topic-modeling literature and in the data science blogosphere LDA is this recieved atomic technique; a black-box tool for modeling documents. In the Stan manual, it is one fairly boring example of a mixture model, only worth talking about explicitly because so many people ask about it.