Probabilistic Machine Learning: An Introduction
probml.github.io
Probabilistic Machine Learning: An Introduction
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Re: Probabilistic Machine Learning: An Introduction
#2[deleted]
Re: Probabilistic Machine Learning: An Introduction
#3while book 1 looks great, it appears that book 2 is still very rough: https://probml.github.io/pml-book/book2.html
Re: Probabilistic Machine Learning: An Introduction
#4Excited to check it out, this was a game changer for me. It turned me on to Gaussian Processes, which I think are a really fun tool.
Re: Probabilistic Machine Learning: An Introduction
#5This is probably my favorite introductory machine learning book. The fact that he places almost everything in the language of graphical models is such a good common ground to build off.
This really sets you up to realize that there is (and should be) a lot more to doing a good job in machine learning than simply minimizing an objective function. The answers you get depend on the model you create as do the questions you can hope to answer.
I don't see a clear list of differences between this new edition. Does anyone know what's new?
Re: Probabilistic Machine Learning: An Introduction
#6Quite excited to read this. Murphy does a great job of explaining concepts from first principles.
Re: Probabilistic Machine Learning: An Introduction
#7The new edition has been split in two parts. The pdf draft (921 pages) and python code [1] of the first part are now available. The table of contents of the second part is here [2].
From the preface:
"By Spring 2020, my draft of the second edition had swollen to about 1600 pages, and I was still not done. At this point, 3 major events happened. First, the COVID-19 pandemic struck, so I decided to “pivot” so I could spend most of my time on COVID-19 modeling. Second, MIT Press told me they could not publish a 1600 page book, and that I would need to split it into two volumes. Third, I decided to recruit several colleagues to help me finish the last ∼ 15% of “missing content”. (See acknowledgements below.)
The result is two new books, “Probabilistic Machine Learning: An Introduction”, which you are currently reading, and “Probabilistic Machine Learning: Advanced Topics”, which is the sequel to this book [Mur22].
Together these two books attempt to present a fairly broad coverage of the field of ML c. 2020, using the same unifying lens of probabilistic modeling and Bayesian decision theory that I used in the first book. Most of the content from the first book has been reused, but it is now split fairly evenly between the two new books. In addition, each book has lots of new material, covering some topics from deep learning, but also advances in other parts of the field, such as generative models, variational inference and reinforcement learning. To make the book more self-contained and useful for students, I have also added some more background content, on topics such as optimization and linear algebra, that was omitted from the first book due to lack of space.
Another major change is that nearly all of the software now uses Python instead of Matlab."
Re: Probabilistic Machine Learning: An Introduction
#8Looking at the table of contents (for someone who is not familiar with the term 'Probabilistic Machine Learning'), is this just covering typical ML methods through the lens of probability?
Re: Probabilistic Machine Learning: An Introduction
#9In my opinion (having read both books and TA’d courses using both), Murphy is a significantly better book than Bishops’s Machine Learning. I’m very excited about a sequel!
Re: Probabilistic Machine Learning: An Introduction
#10This is probably my favorite introductory machine learning book. The fact that he places almost everything in the language of graphical models is such a good common ground to build off. This really sets you up to realize that there is (and should be) a lot more to doing a good job in machine learning than simply minimizing an objective function. The answers you get depend on the model you create as do the questions y…
Agree with you. But none of this is useful for practical (applied) machine learning. I don't want to disappoint you but you can read it as machine learning porn, but otherwise don't waste time on it.